CA2773319A1 - Displaying relationships between concepts to provide classification suggestions via nearest neighbor - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
- G06F16/287—Visualization; Browsing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
- G06F16/3322—Query formulation using system suggestions
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- G—PHYSICS
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
- G06F16/353—Clustering; Classification into predefined classes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
- G06F16/355—Class or cluster creation or modification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
- G06F16/358—Browsing; Visualisation therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/93—Document management systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/954—Navigation, e.g. using categorised browsing
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Abstract
A system (11) and method (50) for displaying relationships between concepts (14c, 14d) to provide classification suggestions via nearest neighbor is provided. Reference concepts (14d) previously classified and a set of uncoded concepts (14c) are provided. At least one uncoded concept (14c) is compared with the reference concepts (14d). One or more of the reference concepts (14d) that are similar to the at least one uncoded concept (14c) are identified. Relationships between the at least one uncoded concept (14c) and the similar reference concept (14d) are depicted on a display for classifying the at least one uncoded concept (14c).
Description
2 PCT/US2010/043506 DISPLAYING RELATIONSHIPS BETWEEN CONCEPTS TO PROVIDE
CLASSIFICATION SÃiGGESTIÃ3NS 'IA NEAREST NEIGHBOR
TECI-INICAL FIELD
This laplic rtit?ra relates in en r ' to using documents as a reference Point and. in particular.. to a s stem and method for displaying relationships between concepts to provide classification suggestions via nearest neighbor, l0 I CKGROUNP RT
HisÃorically, document review durintg the discover. phase of litigation and for other types of legal matters- such as due diligence and re ulatory compliance, have been conducted n aanuall.. Ourin document. rep ies1, iaad.i iduaal. rej iez ers, =generall licensed attr r aeti s. are assigned sets of docwnents for coding, A reviewer must carefully study each document and categorize the document by assigning a code or other marker from a set of descriptive d assit:ica.tiotis, such as '`l ri d:ileged," "responsi e,`{ and "nova-responsii e," `flue classifications Casa affect the disposition of each document, including aadmissibilit-y: into evidence.
During discos ery, document review can potentially affect the outcome of the underlying legal grafter, so consistent and ;accurate results are crucial. Manual document review is tedious and time-consuming. Marking documents is solely at the discretion of each reviewer and inconsistent results ma4' occur due to misunderstarnding, time pressures, fati gtie, or other factors.
A large volume of documents reviewed. often - idi only limited time. can create a. loss of i ientaal focus and a loss of purpose for the resultant classification. Each new reviewer also faces a steep learning curve to become familiar with the legal matter, classification categories, and review x? techniques.
C'urrentl .. with the increasingly i idesl .read movement to electronically stored information (ESI), ranaanual document review is no lon r practicable. The often exponential growth of E:SI exceeds the bounds reasonable for conventional manual human document review and underscores the .need for coraiputer-assisted ESI review tools.
3t:i Conventional ESI review tools have proven inadequate to providing efficient. acccurate, .arid consistent results. For exanip e. i coverR.e ady LIX'._ a Delaware limited taah.ilit company,, custom programs ES) review too s, which conduct semi-automated document review through n ultiple passes over a. document set in ESL .f-o.r.m. During the first pass.
documents are grouped by categoa and basic codes are assigned. Subsequent passes refine and further assign ertacfinvfs.
1rrlÃiple pass review review requires c i?zr/car/ prcaject-specific knor~ledge engineering, which. is only useful for the single project, thereby losing the benefit of any inferred Liowledge or know-how for use in other review proiects.
'I"' us, there remains a need for a system and method for increasing the efficiency of > document review that bootstraps knowledge gained from other reviews while ultimately ensuring independent renewer discretion.
DISC'LOSUR OF THE INVENTION
Document review efficiency can be increased by identifying relationships between reference documents and uncoded documents and providing a suõõestios for classification based to on the relationships. The trrrccoded docunments fo+ra docurrient review project ire identified timid clustered. At least one of the uncoded documents is selected from the clusters and compared with the reference set based on a similarity metric. '11 e reference documents most similar to the selected uncoded document are identified. Classification codes assigned to the similar reference documents can be used to provide suggestions for classification of the selected uncoded 15 document. Furthe:r_ a :machine-t enervated suggestion fora classification codes can be provided 'with a confidence level.
An embodiment provides a system and method for displaying relationships between concepts to provide classification suggestions via nearest neighbor, Reference concepts previously classified and a set of encoded concepts are provided, At least one uncoded concept 20 is compared with the reference concepts. One or more of the reference concepts that are similar to the at least one uncoded concept are identified. Relationships between the at least one uncoded concept and the similar reference concept are depicted on a display for classifying the at least one u ncoded concept.
Still other embodiments of the present invention will become readily apparent to those 25 skilled in the art from the following detailed description, wherein are described embodiments by way of illustrating the best mode contemplated for carrying out the inversion.
As will he realized, the invention is capable of other and different embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and the scope of the present invention. .Accordingly, the drawings rnd detailed description are to be 3tf re4carded as illustrative in mature and not as restrictive, DESCRIPTION OF THE DRAWINGS
FIGURE 1 is a block diagram shcmin a mstmt for displaying relationships beti-veen concepts to provide classification suggestions vi i. nr air est nei hbor.:in accordance with one embodiment.
CLASSIFICATION SÃiGGESTIÃ3NS 'IA NEAREST NEIGHBOR
TECI-INICAL FIELD
This laplic rtit?ra relates in en r ' to using documents as a reference Point and. in particular.. to a s stem and method for displaying relationships between concepts to provide classification suggestions via nearest neighbor, l0 I CKGROUNP RT
HisÃorically, document review durintg the discover. phase of litigation and for other types of legal matters- such as due diligence and re ulatory compliance, have been conducted n aanuall.. Ourin document. rep ies1, iaad.i iduaal. rej iez ers, =generall licensed attr r aeti s. are assigned sets of docwnents for coding, A reviewer must carefully study each document and categorize the document by assigning a code or other marker from a set of descriptive d assit:ica.tiotis, such as '`l ri d:ileged," "responsi e,`{ and "nova-responsii e," `flue classifications Casa affect the disposition of each document, including aadmissibilit-y: into evidence.
During discos ery, document review can potentially affect the outcome of the underlying legal grafter, so consistent and ;accurate results are crucial. Manual document review is tedious and time-consuming. Marking documents is solely at the discretion of each reviewer and inconsistent results ma4' occur due to misunderstarnding, time pressures, fati gtie, or other factors.
A large volume of documents reviewed. often - idi only limited time. can create a. loss of i ientaal focus and a loss of purpose for the resultant classification. Each new reviewer also faces a steep learning curve to become familiar with the legal matter, classification categories, and review x? techniques.
C'urrentl .. with the increasingly i idesl .read movement to electronically stored information (ESI), ranaanual document review is no lon r practicable. The often exponential growth of E:SI exceeds the bounds reasonable for conventional manual human document review and underscores the .need for coraiputer-assisted ESI review tools.
3t:i Conventional ESI review tools have proven inadequate to providing efficient. acccurate, .arid consistent results. For exanip e. i coverR.e ady LIX'._ a Delaware limited taah.ilit company,, custom programs ES) review too s, which conduct semi-automated document review through n ultiple passes over a. document set in ESL .f-o.r.m. During the first pass.
documents are grouped by categoa and basic codes are assigned. Subsequent passes refine and further assign ertacfinvfs.
1rrlÃiple pass review review requires c i?zr/car/ prcaject-specific knor~ledge engineering, which. is only useful for the single project, thereby losing the benefit of any inferred Liowledge or know-how for use in other review proiects.
'I"' us, there remains a need for a system and method for increasing the efficiency of > document review that bootstraps knowledge gained from other reviews while ultimately ensuring independent renewer discretion.
DISC'LOSUR OF THE INVENTION
Document review efficiency can be increased by identifying relationships between reference documents and uncoded documents and providing a suõõestios for classification based to on the relationships. The trrrccoded docunments fo+ra docurrient review project ire identified timid clustered. At least one of the uncoded documents is selected from the clusters and compared with the reference set based on a similarity metric. '11 e reference documents most similar to the selected uncoded document are identified. Classification codes assigned to the similar reference documents can be used to provide suggestions for classification of the selected uncoded 15 document. Furthe:r_ a :machine-t enervated suggestion fora classification codes can be provided 'with a confidence level.
An embodiment provides a system and method for displaying relationships between concepts to provide classification suggestions via nearest neighbor, Reference concepts previously classified and a set of encoded concepts are provided, At least one uncoded concept 20 is compared with the reference concepts. One or more of the reference concepts that are similar to the at least one uncoded concept are identified. Relationships between the at least one uncoded concept and the similar reference concept are depicted on a display for classifying the at least one u ncoded concept.
Still other embodiments of the present invention will become readily apparent to those 25 skilled in the art from the following detailed description, wherein are described embodiments by way of illustrating the best mode contemplated for carrying out the inversion.
As will he realized, the invention is capable of other and different embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and the scope of the present invention. .Accordingly, the drawings rnd detailed description are to be 3tf re4carded as illustrative in mature and not as restrictive, DESCRIPTION OF THE DRAWINGS
FIGURE 1 is a block diagram shcmin a mstmt for displaying relationships beti-veen concepts to provide classification suggestions vi i. nr air est nei hbor.:in accordance with one embodiment.
3 FIGURE 2 is a process flow diagram showing a n ethod for displaying relationships between concepts to provide classification suggestions via. nearest neighbor, in accordance with one em bodi m ent.
FIGURE 3 is a table showing, by way of example, a matrix r napping of uncoded concepts .5 and documents..
FICA' RE 4 Is a block diagram showing, by way of example, measures for select tiny a concept reference subset.
FIGURE: 5 is a process flow- disc rand showing', ( z a of example. meiitod for comparing an encoded concept to reference concepts for use in the method ol.
'FIGURE 2.
FIGURE 6 is a screenshot show-Vi.ng. by w.s,>av of example, sa visual display of reference concepts in relation to uncoded conncepts.
FIGURE 7 is an alternative visual display of the similar reference concepts and encoded concepts.
RE 8 is a process flow diagram showing, by way of example, a method for classifying,, encoded concepts for rase: itn the method of FIGURE 2 .
BES I MODE FOR CARRYING OU=T THE I.' VENT OP"w . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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. . . . . . . . . . . . . . . . . . . . .
The ever-increasing volume of ESI underlies the need for acutomating document review for in pro ed consistency. rand throughput. Token c;lusterin ; ira ioajection utili es reference, or previously classified tokens, which offer knowledge gleaned frog earlier work in similar legal projects, as well as a reference point for classifying uncoded tokens.
The tokens can include word-levve1. svmbol-level. or character-level rt-grams.
raNv terms.
entities,- or concepts. Other tokens, including other atomic parse-level elements, are possible.
An n-gram is a predetermined number of items selected from a source. The items can. include syllables, letters, or words. as well as other items. A raw term is a term that has not been processed or manipulated. Entities further refine nouns and noun phrases into people, places, and things. such as meetings, animals. relaationshipsn and various other objects. Additionally.
entities can represent other parts of grammar associated with. semantic meanings to disambiguate different instances or occurrences of the grammar. Entities can be extracted using entity extraction techniques known in the f eld.
Concepts ,are collections of nouns and noun-phrases with common semantic n reining that can be extracted from. .SI. including doctaa eats, throng pairÃ-ol-speech.
Ãagging. Each concept can represent one or more documents to be classified during a review.
Clustering of the concepts provides an overall :ieN.~-of the document space, which allows users to easily itieaatif documents sharing a common theme.
FIGURE 3 is a table showing, by way of example, a matrix r napping of uncoded concepts .5 and documents..
FICA' RE 4 Is a block diagram showing, by way of example, measures for select tiny a concept reference subset.
FIGURE: 5 is a process flow- disc rand showing', ( z a of example. meiitod for comparing an encoded concept to reference concepts for use in the method ol.
'FIGURE 2.
FIGURE 6 is a screenshot show-Vi.ng. by w.s,>av of example, sa visual display of reference concepts in relation to uncoded conncepts.
FIGURE 7 is an alternative visual display of the similar reference concepts and encoded concepts.
RE 8 is a process flow diagram showing, by way of example, a method for classifying,, encoded concepts for rase: itn the method of FIGURE 2 .
BES I MODE FOR CARRYING OU=T THE I.' VENT OP"w . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . .
The ever-increasing volume of ESI underlies the need for acutomating document review for in pro ed consistency. rand throughput. Token c;lusterin ; ira ioajection utili es reference, or previously classified tokens, which offer knowledge gleaned frog earlier work in similar legal projects, as well as a reference point for classifying uncoded tokens.
The tokens can include word-levve1. svmbol-level. or character-level rt-grams.
raNv terms.
entities,- or concepts. Other tokens, including other atomic parse-level elements, are possible.
An n-gram is a predetermined number of items selected from a source. The items can. include syllables, letters, or words. as well as other items. A raw term is a term that has not been processed or manipulated. Entities further refine nouns and noun phrases into people, places, and things. such as meetings, animals. relaationshipsn and various other objects. Additionally.
entities can represent other parts of grammar associated with. semantic meanings to disambiguate different instances or occurrences of the grammar. Entities can be extracted using entity extraction techniques known in the f eld.
Concepts ,are collections of nouns and noun-phrases with common semantic n reining that can be extracted from. .SI. including doctaa eats, throng pairÃ-ol-speech.
Ãagging. Each concept can represent one or more documents to be classified during a review.
Clustering of the concepts provides an overall :ieN.~-of the document space, which allows users to easily itieaatif documents sharing a common theme.
4 'The clusÃerhm of tokens, for example, concepts, differs from document clustering, vv-hich groups related documents individually. In contrast, concept clustering groups related concepts, which are each representative of one or more related documents Each concept can express an ideas or topic that may not be expressed l individual. documents. A concept is analogous to a > search quer he identif >ing documents associated wvith a particular idea or topic.
A user can determine how particular concepts are related based on the concept cluster' .
Further, users are able to intuitiv'ely' Identify' documents by selecting one or more associated concepts in a cluster. For example, LI user i aay wish to Identify all documents in a particular corpus that are related to car Iria iufacturing. The user can select the concept- car manufacturing" or'-vehicle nvanufacture' within one of the clusters and subsequently, the associated documents are presented. However, during document clustering. a user is first required to select a specific document from wvhich other documents that are similarly related can.
then be ideritilied.
Reference concepts are concepts that have been previously classified and can be used to influence classification of encoded, that is uncl ass.ified, concepts.
Specifically., relationships between the uncoded concepts and the reference concepts can be visuallvy depicted to provide sug estitxas, l-oi instance to a. human reviewer. for citassif 'ing the 3 isually -proximal uncoded concepts Although tokens, such as word-level or character-level n-grams, .raw terms, crib ties, or concepts, can be clustered and displayed, the discussion below will focus on a concept as a particular token.
Complete concept review requires a support environment within which classification cari be performmired. FIGURE I is a block diagram showing a system 10 for displaying relationships between concepts to provide classification suggestions via nearest neighbor, in accordance with one embodiment. By way of illustration, the system 10 operates in a distributed computing environri ent, which includes a pl ur al its. of heterogeneous systems and ESI
sources. Henceforth, a single item of ESI will be referenced as a "documents" although ES! can include other forms of non-document data, as described /Ow. A. back en.d server l l is coupled to a storage device 133, which stores documents 14a, such as encoded docurraents, in the fern of structured or unstructured data, a database 30 for maintaining information about the documents, a lookup database 38 for storing mam -to-many mappings 39 between documents and document features, such as concepts. and a concept document index 40.- which maps documents to concepts. The storage device 13 also stores classified documents 14h, concepts 14c, and reference concepts 14d. Concepts are collections of nouns and noun-phrases with common semantic r reaning. The nouns and noun-phrases can he extracted. from one or more documents in the corpus for rev ievv.
Thus, a single concept can be representative of one or more documents, The reference concepts 14d are each associated with an assigned classification code and considered as classified or coded. Herein after. the terns "classified- and "coded" are used interchangeably Nvith the same intended meanine, unless others ise indicated. A set of reference concepts can be hand-selected > or automatically selected through guided review- which is further discussed.
below Additionally.
the set of reference concepts can be predetermined or can be gerierated dynamically, as" the selected uncoded concepts are classified and subsequently added to the set of reference concepts.
The backend server I I is coupled to an iniranetworlc. 21 and executes a workbench suite 31. for providing a user interface frame ei ork for automated document management, processing, analysis, and classification. In a further embodiment, the backend server 11 can be accessed Via an i.nternetworl 22. The ti orkbenc.h soft are suite 31 includes a document mapper 32 that includes a clustering engine 33, similaril searcher 34. classifier 35, and display generator 36.
Other workbench suite modules are possible.
The clustering engine 33 performs efficient concept scoring and clustering of documents, including uncoded and coded documents. Efficient scoring and clustering is described in conm.rrronlti:-assigned Li. S. Patent :No 7,610,313, the disclosure of which.
is incorporated by reference. Clusters of encoded concepts 14a can be formed and organized along vectors, known a spines, based on a similarity of the clusters, which can be expressed in terms of distance.
During clustering, groupings of related concepts are provided.
In one en-mbodinient_. the clusters can include uncoded and coded concepts.
which are venerated based on a similarity: measure, as discussed in coaniinoanl -owned t..S. Patent Application Serial No. 12/844,810, entitled Svstenn and Method for Display ing Relationships Between Concepts to Provide Classification Suggestions via Inclusion," filed July 27, 2010, pending, and U.S. Patent Application Serial No. 12:844,792., entitled "System and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via injection."
filed July 2/7 2010, pending, the disclosures ofwhich are incorporated by reference.
The similarity searcher 34 identifies the reference concepts 14d that are most similar to selected encoded concepts 14c, clusters, or spines, as further described below with reference to FIGURE 4. For example, the encoded concepts, reference concepts, clusters, and spines can each be represented by a score vector. which includes paired values consisting of a token., such as a terra occurring in that concept, cluster or spine. and the associated score for that, token.
Subsequently, the score sector of the uncoded concept, cluster, or spine is then compared with the score vectors of the reference concepts to identify similar reference concepts.
'The classifier -3*; Provides a r Lachine generaÃe>d suggestion and confidence level for classification of selected uncoded concepts I$d, clusters, or spines, as further described below with reference to FIGURE 7. The display generator 36 arranges the clusters and spines in thematic relationships in a tvvo-dir nension.al. visual display spacer as further described below > beginning with reference to FIGURE 5. Once generated, the visual display space is transmitted to a work client 12 by the backend server 1 1 via the document mapper 32 for prese.nÃin4g to a reviewer on a display 37. The reviewer c< n include ari ndiv dual person vyho is assigned to review and classify one or.more uncoded documents by designating, a code. I-Iere:irrafter, the terms "reviewer" and "custodian" are used i nterchaneea aly with the satne intended meaninig.
unless otherwise indicated. Other types of reviewers are possible, including machine implemented reviewvers.
The document mapper 32 operates on uncoded 14c and coded concepts I4d. which can be retrieved fr"orrx the storage 131, as vv ell as from a plurality of local and remote sources. The local sources include a. local server 1 5 . . which is coupled to a.sÃorage device 16 with documents and corrcepts 1.7. and a local client 18, which is Coupled to a storage device l9 with documents and concepts 20 The local server 15 and local client 18 are interconnected to the back-end server 1 1 and the work client 12 over an intr rr etvzwo.rk- 21. In 'addition, the document wrapper 32 can identify and retrieve concepts from remote sources over an InternetAvork 22, including tile Internet, through a gateway 23 interfaced to the intraneiworl 21, The remote sources include a ren-rote server 24_ which is coupled to a storage device 25 vv:ith documents and concepts 1-6. and a remote client 27, which is coupled to a storage device 28 with documents and concepts 29.
Other document. sources. either local or remote., are possible.
The individual documents 17, 20, 2Ci, 29 include all forms and types of structured and unstructured ESI, including electronic message stores, word processing documents, electronic nail (email) .folders, Web pages, and graphical or multimedia. data.
Notwithstandinlg, the documents could be in the form ofstructurrll organized data, such as stored in a spreadsheet or database.
In one en-rbodiment, the individual documents l$a. 14b, 17, 2.0, 26, 29 include electronic message folders storing email and attachments, such as maintained by the Outlook and Outlook Express products, licensed by Microsoft Corporation, Redmond. W. . The database can be an SQL-based relational database., such as the Oracle database management system, Release 8, licensed by Oracle Corporation, Redwood Shores, CA.
Additionally. the individual concepts 14c, .14d. 17 20, 26, 29 include uncoded concepts and reference concepts. The un.coded concepts, NvIuch are unclassified, represent collections of nouns and noun-phrases that are semantically related and extracted from documents in a document review protect.
The reference concepts are initially uncoded concepts that can represent documents selected from the corpus or other sources of documenÃs. The reference concepts assist in > providing suggestions for classification of the remaining uncoded concepts representative of the document corpus based on visual relationships between the c ncodet concepts and ref erenc e concepts. The reviewer can classify one or more of the remaining uncoded concepts by assigning, a classification code based on the relationships. In a. further enibodinien , the .reference concepts can be used as a training set to form machine-generated suggestions for classiiviris; the remaining uncoded concepts, as further described below with reference to RE 7.
The document corpus for as document review project can he divided into subsets of documents- which. are each provided to a particular ievio~vor as an.
assigrnrne t The ui.ncoded documents are aarnal red to identify concepts, which are subsequently clustered- A classification code can be assigned to each Of the clustered concepts. To maintain consistency., the same codes can be rased aac.ross all concepts reprreseaiting assignments in the document review projject. The classification codes can be determined using taxonomy generation. during, which a list of classification codes can be provided by a reviewer or determined aa.utomatically>. The classification code of as concept can be assigned to the documents associated , ith that concept.
For purposes of legal discover ==, the list, of classification codes can include "privileged."
"responsive," or "anon-responsive." however, other classification codes ai:re possible. The assigned classification codes can be used as suggestions forclass ifi#cation of associated documents. For aa.mple, a. doc airrieni associated ~v.ith three c.c?iic c pts, aicl as sign d a "privileged" classification can also he considered "privileged." Other types of suggestions are possible. A "privileged" document contains iiiforii-mation that is protected bvapricvilege, meaning that the document should not be disclosed or `produced"' to an opposing party.
Disclosing a -privileged- dociiinent can result in an unintentional waiver of?
the subject flatter disclosed. A "responsive" document contains information that is related to the legal mater, while a "non-responsive" document includes information that is not related to the legal matter.
The s~ stem 10 includes individual computer systems. such as the backend server 11s work server 12, server l5, client 18, remote server 2$ and remote client 27.
The individual computer systems are general pu:rpose, programmed digital computing devices consisting. of central processing unit (CP[ )_. random access memory (RAM), non-volatile secondary stora=ge, such as a hard drive or CD ROM drive, network interfaces, and peripheral devices- including User interfacing means, such as a key board and display. The various iniplonion tations of the source code and object and byte codes can be held on a computer-readable:
storage rnediunr, such as a floppy disk. hard drive, digital video disk (L)Vl3), random access memory (RAM), read-only, memory (ROM) and similar storage mediums. For examples program code, including soft3yare programs, and data are loaded into the RANI. for execution and processing by, the CPLU and results are generated for display, output, transmittal, or stora(YOL
Identifying relationships betwee the reference concepts ;grid uncoded concepts includes clustering and similarity measures. FIGURE 2 is a process blow, diagrarn showing a method 50 for displaying relationships between concepts to prmide classification suggestions 6a nearest neighbor, in accordance with one etrrbodimernt. A set of concept clusters is obtained (block 51).
The clusters can include uncoded concepts, and in a.furthe.r embodi.i ment, the clusters can include uncoded and coded concepts.
Clustering of the concepts provides groupings of related concepts and is based on a similarity metric using score y ectors assigned to each concept. The score vectors can be generated using a matrix showing the concepts in relation to documents that contain the .15 concepts. FIGURE 3.is a table showing, by way of example, a matrix mapping 60 of concepts 6.4 and documents 63. The documents 63 are listed along a horizontal dimension 61 of the matrix. while the concepts 64 are. listed along a vertical dimension 62. l Iowes e.r. the placement of the documents 63 and concepts 64 can be reversed. Each cell 65 with-in the matrix 60 .includes a cumulative number of occurrences of each concept within a particular document 63Score vectors can be generated for each docwnent b -v identiAJIm the concepts and associated y eights within that document and ordering the concepts along a vector with the associated concept weight In the matrix 60, the score evertor 66 for a document 63 can be identified as all the concepts included in that document and the associated %\eigh.ts, which are based on the number of occurrences of each concept. Score vectors can also be generated for each concept by identifying the documents that contain that concept and determining a % eight associated % ith each document, The documents and associated weights are then ordered along a vector for each concept; as the concept score vector In the matrix 60, the score vector 67 for a concept can be identified as all the domnents that contain that concept and the associated weights.
In one embodiment, the clustered uncoded concepts can represent a corpus of encoded concepts representative of a document review project. or one or more concepts representative of at least one assignment ofrrncoded concepts The concept corpus can include all. encoded concepts for a. document review project-, while-, each assignment can include a subset of encoded concepts that are representative of one or more documents selected from the corpus and assigned to a reviewer- The corpus can be divided into assignments using assigrrmc.nt criteria, such as custodian or source of the encoded concept- content, document ÃN,-pe, and date. Other criteria are possible.
Returning to the discussion of FIGURE 2, reference concepts can be identified (block 52). "l'h.e reference concepts can include all reference concepts generated for a document review > project, or al.teniatively, a subset of the reference concepts. Obtaining reference concepts is further discussed below with reference to FIGU RE 4.
An encoded concept is selected from one of the clusters in the set and compared against the reference concepts (block 53) to identify one or more reference concepts that are. similar to the selected uncoded concept (block 54). The similar reference concepts are identified based on a safill Urity r measure calculated between the selected uncoded concept and each reference concept. Comparing the selected uncoded concept with the reference concepts is further discussed below with reference to FIGURE 4. Once identified, relationships between the selected encoded concept and the similar reference concepts can be identified (block 55) to provide classification hints, including a suggestion for the selected uncoded concept. as further discussed below with reference to FIGURE 5. Additionally. machine-generated suggestions .f .r classification can be provided (block 56) with an associated confidence level for use in classifying the selected encoded concept. Machine-generated suggestions are .further discussed below with reference to FIGURE 7. Once the selected uncoded concept is assigned a classification code. either by the reviewer or automatically, the newly classified concept can be added to the set of reference concepts for use in classifying further uncoded concepts.
Subsequently, a further uncoded concept can be selected for class if#cattoll using similar reference concepts.
In one embodiment, the classified concepts can be used to classify- those documents represented by that concept. For example. n a. product liability lawsuit, the plaintiff claims that a wood composite manufactured by the defendant induces and harbors mold groNvth. During discovery, all documents within the corpus for the lawsuit and relating to mold should be identified for reviekw.. The concept for mold is clustered and includes a "responsive -, classification code.. which indicates that the noun phrase mold is related to the legal matter.
Upon selection of the mold concept... all documents that include the noun phrase mold can be identified using the mapping matrix. which is described above with reference to FIGURE 3 The responsive classification code assigned to the concept can be used as a suggestion for the document classification, llo%vever, if the. document is represented by multiple concepts with different classification codes- each different code can be considered durine-classification of the document.
In a ftÃrther embodir rent, the concept clusters can be used with. document clusters. v-hich are described in commonly-owvned in U,S. Patent Application Serial No.
121833,860, entitled " System acrd . =Ietlxod for :Displaying Relationships Between Electronically Stored .lnfornration to Provide Classification Suggestions tia 1Ã t:lrrsio filed ftrl 9, 2t)lt?. peÃ
din 1 t.S. 1'aterrt Application Serial No. I2/ 33,872, entitled "System and Metl od for Displaying Relationships Between E1ectron:icali Stored infbrnÃat:ion to Provide Classiticatiron Suggestions \ia1.ojectio.Ãn,'`
.filed July 9, 2010- pending, and U.S. Patent .Application Serial No.
11"833.,880- entitled "Systern and Method for Displaying Relationships Between Electronically Stored Information to Provide Classification Suggestions pia Nearest Neighbor" filed July 9, 2010, pending}, the disclosures of 10 which are incorporated by reference. For example, selecting a concept in t1 e concept cluster display can identr.fi one or more docuÃnc nis with a common idea or topic.
Further selection of one of the documents represented by the selected cluster in the document concept display can identif documents that are imilarl~ related to the content of the selected docummrent. The identified documents can be the same or different as the other documents represented 1 the c_oÃlcep[.
In an even further embodiment, the documents identified from one of the concepts can. be classified automatically as described in conemonly -assi-ned U.S, Patent Application Serial No.
12/83"), 769, entitied " Svstem and Method for Providing a Classification Suggestion for Electronically Stored Information," filed July 9. 2010, pending, the disclosure of ,Nhich is incorporated by reference.
In a further embodiment, similar reference concepts can also be identified for a selected cluster or a selected spine along z Which the clusters are placed.
After the clusters have been generated, one or more encoded concepts can be selected from at least one of the clusters for comparing w :itl a reference concept set or subset. FIGURE 4 is a. block diagram showing, by way of example, measures 70 for selectin ; a concept reference subset 71. The subset of reference concepts 71 can be previously defined 74 and maintained for related document review proiects or can be specifically generated for each.
review project. A
predefined reference subset 74 provides knowledge previously obtained during tine related document review project to increase efficiency, accur icy. and consistency.
Reference subsets newly generated for each. review project can include arbitra y 72 or customized 73 reference sral sets that are determined automatically or by a human reviewer. An arbitrar reference subset 72 includes reference concepts randorrrl selected for inclusion in the reference subset. A
customized reference subset 73 includes reference concepts specifically selected for inclusion i.n the reference subset based on criteria, such as r .o=iea4 .r preference, classification category.
document source, content, and review project. Other criteria are possible.
The subset of reference concepts, whether predetermined or newly generated.
should be selected from m a set of reference concepts that are representative of docuÃ.
ents in the doc mein > corpus for a rev'iew project in NvIiich data organization or classiiication is desired. Guided review aassists a reviewer or other user in identifying reference concepts that are rep. eserlt Ãlit e of the.
corpus for use in classif wing encoded concepts. During guided review, the uncoded concepts that are dissiranrilar to all otht = Ur coded concepts are identified based on a similarity threshold In one embodiment, the dissimilarit can be determined as the cos o of the score vectors for the lit uncoded concepts, à ther à ethods for determining dissimilarity are possible. Identifying the dissimilar concepts provides a group of conc pts that are representative of the document in a corpus fora review project. Each identified dissimilar concept is then classified b-'.1 assigning a particular classification code based on the content of the associated documents to collectively generate the reference concepts. Guided review can he performed by a reviewer, a machine, or a 15 combination of the reviewer and naachirie.
Other methods f or generati.rig reference concepts .for a document review project using guided review are possible.- including claast ;a-ing A set of encoded, documents to he classified is clustered, as described in commonly-assigned U. Patent No. 7,61.0,313T the disclosure of which is incorporated by reference. A plurality of the clustered uncoded concepts are selected based on 20 selection criteria, such ,is cluster centers or sample clusters. The cluster centers can be used to identif uncoded concepts in a cluster that are most similar or dissimilar to the citaste.r Center.
The selected uncoded concepts are then assigned classification codes. In a further embodiment, sample clusters can be used to generate reference concepts by selecting one or more sample clusters based on cluster relation criteria. such as sire, content, similarity:, or dissimilarity. The 25 encoded concepts in the selected sample clusters are then selected for cl,,xssifioation- by assigning classification codes. The classified concepts represent reference concepts for the document review project. The number of reference concepts can be determined automatically or 1w a rev ievver. Other method for selecting concepts for use as ref'cerence concepts are possible.
An. uncoded concept selected from one of the clusters can be compared to the reference 3 3t concepts to identil similar reference concepts For use in providing suggestions regarding classification of the selected uncoded concept. FIGURE 5 is a process flow diagram showing..
1 way of example, a method 80 for comparing an uncoded concept to reference concepts for use in the method of FIGURE 2. The encoded concept is selected from. a cluster (block S1) and applied to the reference concepts (block 82), The reference concepts can include all reference concepts for a document review project or a subset of the reference concepts Each of the reference concepts and the selected uncoded concept can be represented by a score vector having paired valcres of documents associated with that concept and associated scores.A similarity between the uncoded Concept and each reference concept is determined (block 83) as the cos c5 of the score vectors for tfre rarccaded concept and reference concept being compared and is equivalent to the inner product between the score vectors. In the described embodiment, the cos is calculated in accordance with the eelsÃatioà :
1`~= fit'/
---------------------Cos v;
where cos o comprises a siÃnilarity between Luacoded concept A and reference concept B, S., comprises a score vector for uncoded concept A, and Kt; comprises a score vector- for reference concept B. Other forms of determining s.r iil.arity using a distance metric are possible, as could be recognized bb: one skilled in the art. including using, Euclidean distance.
One or more of the .reference concepts that are most similar to the selected urrrcoded concept, based on the similarity metrics are identified. The most similar reference concepts can be identified by satisfying a predetermined threshold of sinhilarit . Other methods for determining the similar reference concepts are possible, such as setting a predetermined absolute number of the most similar reference concepts. The classification codes of the identified similar reference concepts can be used as suggestions for cla ;si i ing the selected uncoded concept. as further described below with reference to FIGURE S. Once identified, the similar reference concept can be used to provide suggestions re arding classification of the selected uncoded coÃ:rcept, as further described below with reference to FIGURES 6 'and 7.
The similar reference concepts can be displayed with the clusters of encoded concepts.
In the display, the similar reference concepts can be prow ided as a list, while the clusters can be can be organized along spines of thematically related clusters, as described in coin only--a. sivfned U.S..Patent No. 7.271 4, the disclosure of which is incorporated by reference. The spines can be positioned in relation to other cluster spines based on a theme shared by those cluster spines, as described in conararonly-assigned U .S. Patent No. 7,610,13 13, the disclosure of which is incorporated by reference. Other displays of the clusters and similar reference.
documents are possible.
Organizing the. clusters into spines and groups of cluster spines provides an individual reviewer with a display that presents the concepts according to a thence while maximizing the number of relationships depleted beetz ecn the concept' FIGURE 6.is a screeenshot 90 showi.ng, by way of example, a visual display 91 of similar reference. concepts 94 and uncoded concepts 94. Clusters 92 of the uaicoded concepts 93 can be located along a spine, which is a. vector, based on a similarity of the encoded concepts 93 in the clusters 92. The encoded concepts 93 are each represented by a smaller circle, within the cluster's, 92.
> Similar reference concepts 94 identified for a selected uncoded concept 93 can. be displayed in a list 95 by document title or other identifier. Also. cl asslfic 1 on codes 96 associa.t d with the similar reference concepts 94 can be displayed as circles havin a diamond shape within the boundary of the ci:rcle. The classification codes 96 can include "privileged, " responsive." and "non-responsive" codes, as well as other codes. The different classification codes 96 can each be represented by a color,. such as blue for "privileged"
reference documents and yellow for 'rai>n-respons ~ e" reference concepts. Other display rel .resentalions Of iti uncoded concepts, similar reference concepts. and classification codes are possible, including by symbols and shapes.
The classification codes 96 of the similar reference concepts 94 can provide Suggestions for classifying, the selected uncoded concept based on. factors, such as a number ofdilierent classification codes for the similar reference concepts and a number of similar reference concepts associated with each classification code. For example, the list of reference concepts includes four similar reference concepts :identified for a. particular uncoded concept... Three of'the reference concepts are classified as privileged." while one is classified as "non-responsive," In making a decision to assign a classification code to a selected encoded concept, the reviewer can consider classification .factors based on the similar reference concepts, such as a presence or absence of similar reference concepts with different classificatioan codes and a quantity of the similar reference concepts for each classification code. Other classification factors are possible.
in the current example, the display 91 provides suggestions, including the number of "privileged" similar reference concepts. the number of "non-responsive"
similar reference concepts- and the absence of other classification codes of similar reference concepts. Based on the number of "privileged.- similar reference concepts compared to the number of "n.on-responsive similar reference concepts. the reviewer may be more inclined to classif\ the selected uncoded concepts as "privileged." Alternatively., the revievver m ry wish to further review the selected uncoded concept based on the multiple classification codes of the similar reference concepts.. Other classification codes and combinations ol'classification codes are possible. The reviewer can utilize the suggestions provided by the similar reference concepts to assign a classification to the selected uncoded concept. In a further embodiment. the now classified and previously uncoded concept can be added to the set of reference concepts for use in classif wing other uncoiled concepts.
In a further enabodinient, similar reference concepts can be identified for- a cl us-ter or spine to provide str estions for clnssrf 'ir tq tl e clarstcr' artd spire.
1'or a cl.atsterr the: sir : lar reference concepts are identified based on. a comparison of a score vector for the cluster, which. is YCprese.nÃettive of the cluster center and the reference concept score vectors. Meanv w1-ii1e, identif vine sirnilar reference concepts for a spine is based on a comparison between the score vector for the spine.. which is based on the cluster center of all the clusters along that spine, and the reference: concept score vectors. Once identified, the similar reference concepts are used for classifying; the cluster or spine.
In an even .furthe = embodiment, the encoded concepts, including the selected uncoiled concept, and the similar reference concepts can be displayed as a concept list. FIGURE 7 is a screenshot 100 showy ing_ by way of example. an alternative: visual display of the similar reference concepts 105 and uncoded concepts 102. The trncoded concepts 1. 02 can be provided as a list in an uncoded concept box 101, such as an email inbox. 'T' he uncoded concepts 102 can be identified and organized based on nsetadata about the uncoded concept or information provided in the associated documeants.
At least one of the encoded concepts can be selected and displayed in a concept viewing box 10$. The selected uncoded concept can be identified in the list 101 using a selection indicator (not shovvn), including a syn-mbol, font, or higlrlight:ing. Other selection indicators and encoded concept factors are possible. Once identified, the selected uncoded concept can be compared to a set of reference concepts to identif~, the reference concepts 85 most similar. The identified similar reference concepts 105 can be displayed belovv the concept viewing box 104 with an associated classification code 103. The classification code of the similar reference concept 105 can be used as a suggestion for classill ing the selected uncoded concept. After assigning a classification code. a representation 103 of the classification can be provided in the display with the selected uncoded concept. In a further embodiment, the now classified and previously uncoded concept can be added to the set of reference concepts.
Similar reference concepts can be used as suggestions to indicate a need for manual review of the encoded concepts, vv hen review may be unnecessary, and hints for classifying the uncoded concepts, clusters, or spines. Additional information Cary he generated to assist a.
reviewer in making classification decisions for the trncoded concepts, such as a machine-generated confidence level associated with a suggested classification coder as described in common-assigned ITS. Patent Application Serial No. 1211118'44,785- entitled "System and Method for Providing a Classification Suggestion for Concepts," filed on Jule- 277 201 i1, pending, the disclosure of which is incorporated by reference.
The machine-generated suggestion for classification and associated confidence level can be determined by a classifier, FIGURE 8 is a process flow diagrat 110 showing, by ivay of
A user can determine how particular concepts are related based on the concept cluster' .
Further, users are able to intuitiv'ely' Identify' documents by selecting one or more associated concepts in a cluster. For example, LI user i aay wish to Identify all documents in a particular corpus that are related to car Iria iufacturing. The user can select the concept- car manufacturing" or'-vehicle nvanufacture' within one of the clusters and subsequently, the associated documents are presented. However, during document clustering. a user is first required to select a specific document from wvhich other documents that are similarly related can.
then be ideritilied.
Reference concepts are concepts that have been previously classified and can be used to influence classification of encoded, that is uncl ass.ified, concepts.
Specifically., relationships between the uncoded concepts and the reference concepts can be visuallvy depicted to provide sug estitxas, l-oi instance to a. human reviewer. for citassif 'ing the 3 isually -proximal uncoded concepts Although tokens, such as word-level or character-level n-grams, .raw terms, crib ties, or concepts, can be clustered and displayed, the discussion below will focus on a concept as a particular token.
Complete concept review requires a support environment within which classification cari be performmired. FIGURE I is a block diagram showing a system 10 for displaying relationships between concepts to provide classification suggestions via nearest neighbor, in accordance with one embodiment. By way of illustration, the system 10 operates in a distributed computing environri ent, which includes a pl ur al its. of heterogeneous systems and ESI
sources. Henceforth, a single item of ESI will be referenced as a "documents" although ES! can include other forms of non-document data, as described /Ow. A. back en.d server l l is coupled to a storage device 133, which stores documents 14a, such as encoded docurraents, in the fern of structured or unstructured data, a database 30 for maintaining information about the documents, a lookup database 38 for storing mam -to-many mappings 39 between documents and document features, such as concepts. and a concept document index 40.- which maps documents to concepts. The storage device 13 also stores classified documents 14h, concepts 14c, and reference concepts 14d. Concepts are collections of nouns and noun-phrases with common semantic r reaning. The nouns and noun-phrases can he extracted. from one or more documents in the corpus for rev ievv.
Thus, a single concept can be representative of one or more documents, The reference concepts 14d are each associated with an assigned classification code and considered as classified or coded. Herein after. the terns "classified- and "coded" are used interchangeably Nvith the same intended meanine, unless others ise indicated. A set of reference concepts can be hand-selected > or automatically selected through guided review- which is further discussed.
below Additionally.
the set of reference concepts can be predetermined or can be gerierated dynamically, as" the selected uncoded concepts are classified and subsequently added to the set of reference concepts.
The backend server I I is coupled to an iniranetworlc. 21 and executes a workbench suite 31. for providing a user interface frame ei ork for automated document management, processing, analysis, and classification. In a further embodiment, the backend server 11 can be accessed Via an i.nternetworl 22. The ti orkbenc.h soft are suite 31 includes a document mapper 32 that includes a clustering engine 33, similaril searcher 34. classifier 35, and display generator 36.
Other workbench suite modules are possible.
The clustering engine 33 performs efficient concept scoring and clustering of documents, including uncoded and coded documents. Efficient scoring and clustering is described in conm.rrronlti:-assigned Li. S. Patent :No 7,610,313, the disclosure of which.
is incorporated by reference. Clusters of encoded concepts 14a can be formed and organized along vectors, known a spines, based on a similarity of the clusters, which can be expressed in terms of distance.
During clustering, groupings of related concepts are provided.
In one en-mbodinient_. the clusters can include uncoded and coded concepts.
which are venerated based on a similarity: measure, as discussed in coaniinoanl -owned t..S. Patent Application Serial No. 12/844,810, entitled Svstenn and Method for Display ing Relationships Between Concepts to Provide Classification Suggestions via Inclusion," filed July 27, 2010, pending, and U.S. Patent Application Serial No. 12:844,792., entitled "System and Method for Displaying Relationships Between Concepts to Provide Classification Suggestions via injection."
filed July 2/7 2010, pending, the disclosures ofwhich are incorporated by reference.
The similarity searcher 34 identifies the reference concepts 14d that are most similar to selected encoded concepts 14c, clusters, or spines, as further described below with reference to FIGURE 4. For example, the encoded concepts, reference concepts, clusters, and spines can each be represented by a score vector. which includes paired values consisting of a token., such as a terra occurring in that concept, cluster or spine. and the associated score for that, token.
Subsequently, the score sector of the uncoded concept, cluster, or spine is then compared with the score vectors of the reference concepts to identify similar reference concepts.
'The classifier -3*; Provides a r Lachine generaÃe>d suggestion and confidence level for classification of selected uncoded concepts I$d, clusters, or spines, as further described below with reference to FIGURE 7. The display generator 36 arranges the clusters and spines in thematic relationships in a tvvo-dir nension.al. visual display spacer as further described below > beginning with reference to FIGURE 5. Once generated, the visual display space is transmitted to a work client 12 by the backend server 1 1 via the document mapper 32 for prese.nÃin4g to a reviewer on a display 37. The reviewer c< n include ari ndiv dual person vyho is assigned to review and classify one or.more uncoded documents by designating, a code. I-Iere:irrafter, the terms "reviewer" and "custodian" are used i nterchaneea aly with the satne intended meaninig.
unless otherwise indicated. Other types of reviewers are possible, including machine implemented reviewvers.
The document mapper 32 operates on uncoded 14c and coded concepts I4d. which can be retrieved fr"orrx the storage 131, as vv ell as from a plurality of local and remote sources. The local sources include a. local server 1 5 . . which is coupled to a.sÃorage device 16 with documents and corrcepts 1.7. and a local client 18, which is Coupled to a storage device l9 with documents and concepts 20 The local server 15 and local client 18 are interconnected to the back-end server 1 1 and the work client 12 over an intr rr etvzwo.rk- 21. In 'addition, the document wrapper 32 can identify and retrieve concepts from remote sources over an InternetAvork 22, including tile Internet, through a gateway 23 interfaced to the intraneiworl 21, The remote sources include a ren-rote server 24_ which is coupled to a storage device 25 vv:ith documents and concepts 1-6. and a remote client 27, which is coupled to a storage device 28 with documents and concepts 29.
Other document. sources. either local or remote., are possible.
The individual documents 17, 20, 2Ci, 29 include all forms and types of structured and unstructured ESI, including electronic message stores, word processing documents, electronic nail (email) .folders, Web pages, and graphical or multimedia. data.
Notwithstandinlg, the documents could be in the form ofstructurrll organized data, such as stored in a spreadsheet or database.
In one en-rbodiment, the individual documents l$a. 14b, 17, 2.0, 26, 29 include electronic message folders storing email and attachments, such as maintained by the Outlook and Outlook Express products, licensed by Microsoft Corporation, Redmond. W. . The database can be an SQL-based relational database., such as the Oracle database management system, Release 8, licensed by Oracle Corporation, Redwood Shores, CA.
Additionally. the individual concepts 14c, .14d. 17 20, 26, 29 include uncoded concepts and reference concepts. The un.coded concepts, NvIuch are unclassified, represent collections of nouns and noun-phrases that are semantically related and extracted from documents in a document review protect.
The reference concepts are initially uncoded concepts that can represent documents selected from the corpus or other sources of documenÃs. The reference concepts assist in > providing suggestions for classification of the remaining uncoded concepts representative of the document corpus based on visual relationships between the c ncodet concepts and ref erenc e concepts. The reviewer can classify one or more of the remaining uncoded concepts by assigning, a classification code based on the relationships. In a. further enibodinien , the .reference concepts can be used as a training set to form machine-generated suggestions for classiiviris; the remaining uncoded concepts, as further described below with reference to RE 7.
The document corpus for as document review project can he divided into subsets of documents- which. are each provided to a particular ievio~vor as an.
assigrnrne t The ui.ncoded documents are aarnal red to identify concepts, which are subsequently clustered- A classification code can be assigned to each Of the clustered concepts. To maintain consistency., the same codes can be rased aac.ross all concepts reprreseaiting assignments in the document review projject. The classification codes can be determined using taxonomy generation. during, which a list of classification codes can be provided by a reviewer or determined aa.utomatically>. The classification code of as concept can be assigned to the documents associated , ith that concept.
For purposes of legal discover ==, the list, of classification codes can include "privileged."
"responsive," or "anon-responsive." however, other classification codes ai:re possible. The assigned classification codes can be used as suggestions forclass ifi#cation of associated documents. For aa.mple, a. doc airrieni associated ~v.ith three c.c?iic c pts, aicl as sign d a "privileged" classification can also he considered "privileged." Other types of suggestions are possible. A "privileged" document contains iiiforii-mation that is protected bvapricvilege, meaning that the document should not be disclosed or `produced"' to an opposing party.
Disclosing a -privileged- dociiinent can result in an unintentional waiver of?
the subject flatter disclosed. A "responsive" document contains information that is related to the legal mater, while a "non-responsive" document includes information that is not related to the legal matter.
The s~ stem 10 includes individual computer systems. such as the backend server 11s work server 12, server l5, client 18, remote server 2$ and remote client 27.
The individual computer systems are general pu:rpose, programmed digital computing devices consisting. of central processing unit (CP[ )_. random access memory (RAM), non-volatile secondary stora=ge, such as a hard drive or CD ROM drive, network interfaces, and peripheral devices- including User interfacing means, such as a key board and display. The various iniplonion tations of the source code and object and byte codes can be held on a computer-readable:
storage rnediunr, such as a floppy disk. hard drive, digital video disk (L)Vl3), random access memory (RAM), read-only, memory (ROM) and similar storage mediums. For examples program code, including soft3yare programs, and data are loaded into the RANI. for execution and processing by, the CPLU and results are generated for display, output, transmittal, or stora(YOL
Identifying relationships betwee the reference concepts ;grid uncoded concepts includes clustering and similarity measures. FIGURE 2 is a process blow, diagrarn showing a method 50 for displaying relationships between concepts to prmide classification suggestions 6a nearest neighbor, in accordance with one etrrbodimernt. A set of concept clusters is obtained (block 51).
The clusters can include uncoded concepts, and in a.furthe.r embodi.i ment, the clusters can include uncoded and coded concepts.
Clustering of the concepts provides groupings of related concepts and is based on a similarity metric using score y ectors assigned to each concept. The score vectors can be generated using a matrix showing the concepts in relation to documents that contain the .15 concepts. FIGURE 3.is a table showing, by way of example, a matrix mapping 60 of concepts 6.4 and documents 63. The documents 63 are listed along a horizontal dimension 61 of the matrix. while the concepts 64 are. listed along a vertical dimension 62. l Iowes e.r. the placement of the documents 63 and concepts 64 can be reversed. Each cell 65 with-in the matrix 60 .includes a cumulative number of occurrences of each concept within a particular document 63Score vectors can be generated for each docwnent b -v identiAJIm the concepts and associated y eights within that document and ordering the concepts along a vector with the associated concept weight In the matrix 60, the score evertor 66 for a document 63 can be identified as all the concepts included in that document and the associated %\eigh.ts, which are based on the number of occurrences of each concept. Score vectors can also be generated for each concept by identifying the documents that contain that concept and determining a % eight associated % ith each document, The documents and associated weights are then ordered along a vector for each concept; as the concept score vector In the matrix 60, the score vector 67 for a concept can be identified as all the domnents that contain that concept and the associated weights.
In one embodiment, the clustered uncoded concepts can represent a corpus of encoded concepts representative of a document review project. or one or more concepts representative of at least one assignment ofrrncoded concepts The concept corpus can include all. encoded concepts for a. document review project-, while-, each assignment can include a subset of encoded concepts that are representative of one or more documents selected from the corpus and assigned to a reviewer- The corpus can be divided into assignments using assigrrmc.nt criteria, such as custodian or source of the encoded concept- content, document ÃN,-pe, and date. Other criteria are possible.
Returning to the discussion of FIGURE 2, reference concepts can be identified (block 52). "l'h.e reference concepts can include all reference concepts generated for a document review > project, or al.teniatively, a subset of the reference concepts. Obtaining reference concepts is further discussed below with reference to FIGU RE 4.
An encoded concept is selected from one of the clusters in the set and compared against the reference concepts (block 53) to identify one or more reference concepts that are. similar to the selected uncoded concept (block 54). The similar reference concepts are identified based on a safill Urity r measure calculated between the selected uncoded concept and each reference concept. Comparing the selected uncoded concept with the reference concepts is further discussed below with reference to FIGURE 4. Once identified, relationships between the selected encoded concept and the similar reference concepts can be identified (block 55) to provide classification hints, including a suggestion for the selected uncoded concept. as further discussed below with reference to FIGURE 5. Additionally. machine-generated suggestions .f .r classification can be provided (block 56) with an associated confidence level for use in classifying the selected encoded concept. Machine-generated suggestions are .further discussed below with reference to FIGURE 7. Once the selected uncoded concept is assigned a classification code. either by the reviewer or automatically, the newly classified concept can be added to the set of reference concepts for use in classifying further uncoded concepts.
Subsequently, a further uncoded concept can be selected for class if#cattoll using similar reference concepts.
In one embodiment, the classified concepts can be used to classify- those documents represented by that concept. For example. n a. product liability lawsuit, the plaintiff claims that a wood composite manufactured by the defendant induces and harbors mold groNvth. During discovery, all documents within the corpus for the lawsuit and relating to mold should be identified for reviekw.. The concept for mold is clustered and includes a "responsive -, classification code.. which indicates that the noun phrase mold is related to the legal matter.
Upon selection of the mold concept... all documents that include the noun phrase mold can be identified using the mapping matrix. which is described above with reference to FIGURE 3 The responsive classification code assigned to the concept can be used as a suggestion for the document classification, llo%vever, if the. document is represented by multiple concepts with different classification codes- each different code can be considered durine-classification of the document.
In a ftÃrther embodir rent, the concept clusters can be used with. document clusters. v-hich are described in commonly-owvned in U,S. Patent Application Serial No.
121833,860, entitled " System acrd . =Ietlxod for :Displaying Relationships Between Electronically Stored .lnfornration to Provide Classification Suggestions tia 1Ã t:lrrsio filed ftrl 9, 2t)lt?. peÃ
din 1 t.S. 1'aterrt Application Serial No. I2/ 33,872, entitled "System and Metl od for Displaying Relationships Between E1ectron:icali Stored infbrnÃat:ion to Provide Classiticatiron Suggestions \ia1.ojectio.Ãn,'`
.filed July 9, 2010- pending, and U.S. Patent .Application Serial No.
11"833.,880- entitled "Systern and Method for Displaying Relationships Between Electronically Stored Information to Provide Classification Suggestions pia Nearest Neighbor" filed July 9, 2010, pending}, the disclosures of 10 which are incorporated by reference. For example, selecting a concept in t1 e concept cluster display can identr.fi one or more docuÃnc nis with a common idea or topic.
Further selection of one of the documents represented by the selected cluster in the document concept display can identif documents that are imilarl~ related to the content of the selected docummrent. The identified documents can be the same or different as the other documents represented 1 the c_oÃlcep[.
In an even further embodiment, the documents identified from one of the concepts can. be classified automatically as described in conemonly -assi-ned U.S, Patent Application Serial No.
12/83"), 769, entitied " Svstem and Method for Providing a Classification Suggestion for Electronically Stored Information," filed July 9. 2010, pending, the disclosure of ,Nhich is incorporated by reference.
In a further embodiment, similar reference concepts can also be identified for a selected cluster or a selected spine along z Which the clusters are placed.
After the clusters have been generated, one or more encoded concepts can be selected from at least one of the clusters for comparing w :itl a reference concept set or subset. FIGURE 4 is a. block diagram showing, by way of example, measures 70 for selectin ; a concept reference subset 71. The subset of reference concepts 71 can be previously defined 74 and maintained for related document review proiects or can be specifically generated for each.
review project. A
predefined reference subset 74 provides knowledge previously obtained during tine related document review project to increase efficiency, accur icy. and consistency.
Reference subsets newly generated for each. review project can include arbitra y 72 or customized 73 reference sral sets that are determined automatically or by a human reviewer. An arbitrar reference subset 72 includes reference concepts randorrrl selected for inclusion in the reference subset. A
customized reference subset 73 includes reference concepts specifically selected for inclusion i.n the reference subset based on criteria, such as r .o=iea4 .r preference, classification category.
document source, content, and review project. Other criteria are possible.
The subset of reference concepts, whether predetermined or newly generated.
should be selected from m a set of reference concepts that are representative of docuÃ.
ents in the doc mein > corpus for a rev'iew project in NvIiich data organization or classiiication is desired. Guided review aassists a reviewer or other user in identifying reference concepts that are rep. eserlt Ãlit e of the.
corpus for use in classif wing encoded concepts. During guided review, the uncoded concepts that are dissiranrilar to all otht = Ur coded concepts are identified based on a similarity threshold In one embodiment, the dissimilarit can be determined as the cos o of the score vectors for the lit uncoded concepts, à ther à ethods for determining dissimilarity are possible. Identifying the dissimilar concepts provides a group of conc pts that are representative of the document in a corpus fora review project. Each identified dissimilar concept is then classified b-'.1 assigning a particular classification code based on the content of the associated documents to collectively generate the reference concepts. Guided review can he performed by a reviewer, a machine, or a 15 combination of the reviewer and naachirie.
Other methods f or generati.rig reference concepts .for a document review project using guided review are possible.- including claast ;a-ing A set of encoded, documents to he classified is clustered, as described in commonly-assigned U. Patent No. 7,61.0,313T the disclosure of which is incorporated by reference. A plurality of the clustered uncoded concepts are selected based on 20 selection criteria, such ,is cluster centers or sample clusters. The cluster centers can be used to identif uncoded concepts in a cluster that are most similar or dissimilar to the citaste.r Center.
The selected uncoded concepts are then assigned classification codes. In a further embodiment, sample clusters can be used to generate reference concepts by selecting one or more sample clusters based on cluster relation criteria. such as sire, content, similarity:, or dissimilarity. The 25 encoded concepts in the selected sample clusters are then selected for cl,,xssifioation- by assigning classification codes. The classified concepts represent reference concepts for the document review project. The number of reference concepts can be determined automatically or 1w a rev ievver. Other method for selecting concepts for use as ref'cerence concepts are possible.
An. uncoded concept selected from one of the clusters can be compared to the reference 3 3t concepts to identil similar reference concepts For use in providing suggestions regarding classification of the selected uncoded concept. FIGURE 5 is a process flow diagram showing..
1 way of example, a method 80 for comparing an uncoded concept to reference concepts for use in the method of FIGURE 2. The encoded concept is selected from. a cluster (block S1) and applied to the reference concepts (block 82), The reference concepts can include all reference concepts for a document review project or a subset of the reference concepts Each of the reference concepts and the selected uncoded concept can be represented by a score vector having paired valcres of documents associated with that concept and associated scores.A similarity between the uncoded Concept and each reference concept is determined (block 83) as the cos c5 of the score vectors for tfre rarccaded concept and reference concept being compared and is equivalent to the inner product between the score vectors. In the described embodiment, the cos is calculated in accordance with the eelsÃatioà :
1`~= fit'/
---------------------Cos v;
where cos o comprises a siÃnilarity between Luacoded concept A and reference concept B, S., comprises a score vector for uncoded concept A, and Kt; comprises a score vector- for reference concept B. Other forms of determining s.r iil.arity using a distance metric are possible, as could be recognized bb: one skilled in the art. including using, Euclidean distance.
One or more of the .reference concepts that are most similar to the selected urrrcoded concept, based on the similarity metrics are identified. The most similar reference concepts can be identified by satisfying a predetermined threshold of sinhilarit . Other methods for determining the similar reference concepts are possible, such as setting a predetermined absolute number of the most similar reference concepts. The classification codes of the identified similar reference concepts can be used as suggestions for cla ;si i ing the selected uncoded concept. as further described below with reference to FIGURE S. Once identified, the similar reference concept can be used to provide suggestions re arding classification of the selected uncoded coÃ:rcept, as further described below with reference to FIGURES 6 'and 7.
The similar reference concepts can be displayed with the clusters of encoded concepts.
In the display, the similar reference concepts can be prow ided as a list, while the clusters can be can be organized along spines of thematically related clusters, as described in coin only--a. sivfned U.S..Patent No. 7.271 4, the disclosure of which is incorporated by reference. The spines can be positioned in relation to other cluster spines based on a theme shared by those cluster spines, as described in conararonly-assigned U .S. Patent No. 7,610,13 13, the disclosure of which is incorporated by reference. Other displays of the clusters and similar reference.
documents are possible.
Organizing the. clusters into spines and groups of cluster spines provides an individual reviewer with a display that presents the concepts according to a thence while maximizing the number of relationships depleted beetz ecn the concept' FIGURE 6.is a screeenshot 90 showi.ng, by way of example, a visual display 91 of similar reference. concepts 94 and uncoded concepts 94. Clusters 92 of the uaicoded concepts 93 can be located along a spine, which is a. vector, based on a similarity of the encoded concepts 93 in the clusters 92. The encoded concepts 93 are each represented by a smaller circle, within the cluster's, 92.
> Similar reference concepts 94 identified for a selected uncoded concept 93 can. be displayed in a list 95 by document title or other identifier. Also. cl asslfic 1 on codes 96 associa.t d with the similar reference concepts 94 can be displayed as circles havin a diamond shape within the boundary of the ci:rcle. The classification codes 96 can include "privileged, " responsive." and "non-responsive" codes, as well as other codes. The different classification codes 96 can each be represented by a color,. such as blue for "privileged"
reference documents and yellow for 'rai>n-respons ~ e" reference concepts. Other display rel .resentalions Of iti uncoded concepts, similar reference concepts. and classification codes are possible, including by symbols and shapes.
The classification codes 96 of the similar reference concepts 94 can provide Suggestions for classifying, the selected uncoded concept based on. factors, such as a number ofdilierent classification codes for the similar reference concepts and a number of similar reference concepts associated with each classification code. For example, the list of reference concepts includes four similar reference concepts :identified for a. particular uncoded concept... Three of'the reference concepts are classified as privileged." while one is classified as "non-responsive," In making a decision to assign a classification code to a selected encoded concept, the reviewer can consider classification .factors based on the similar reference concepts, such as a presence or absence of similar reference concepts with different classificatioan codes and a quantity of the similar reference concepts for each classification code. Other classification factors are possible.
in the current example, the display 91 provides suggestions, including the number of "privileged" similar reference concepts. the number of "non-responsive"
similar reference concepts- and the absence of other classification codes of similar reference concepts. Based on the number of "privileged.- similar reference concepts compared to the number of "n.on-responsive similar reference concepts. the reviewer may be more inclined to classif\ the selected uncoded concepts as "privileged." Alternatively., the revievver m ry wish to further review the selected uncoded concept based on the multiple classification codes of the similar reference concepts.. Other classification codes and combinations ol'classification codes are possible. The reviewer can utilize the suggestions provided by the similar reference concepts to assign a classification to the selected uncoded concept. In a further embodiment. the now classified and previously uncoded concept can be added to the set of reference concepts for use in classif wing other uncoiled concepts.
In a further enabodinient, similar reference concepts can be identified for- a cl us-ter or spine to provide str estions for clnssrf 'ir tq tl e clarstcr' artd spire.
1'or a cl.atsterr the: sir : lar reference concepts are identified based on. a comparison of a score vector for the cluster, which. is YCprese.nÃettive of the cluster center and the reference concept score vectors. Meanv w1-ii1e, identif vine sirnilar reference concepts for a spine is based on a comparison between the score vector for the spine.. which is based on the cluster center of all the clusters along that spine, and the reference: concept score vectors. Once identified, the similar reference concepts are used for classifying; the cluster or spine.
In an even .furthe = embodiment, the encoded concepts, including the selected uncoiled concept, and the similar reference concepts can be displayed as a concept list. FIGURE 7 is a screenshot 100 showy ing_ by way of example. an alternative: visual display of the similar reference concepts 105 and uncoded concepts 102. The trncoded concepts 1. 02 can be provided as a list in an uncoded concept box 101, such as an email inbox. 'T' he uncoded concepts 102 can be identified and organized based on nsetadata about the uncoded concept or information provided in the associated documeants.
At least one of the encoded concepts can be selected and displayed in a concept viewing box 10$. The selected uncoded concept can be identified in the list 101 using a selection indicator (not shovvn), including a syn-mbol, font, or higlrlight:ing. Other selection indicators and encoded concept factors are possible. Once identified, the selected uncoded concept can be compared to a set of reference concepts to identif~, the reference concepts 85 most similar. The identified similar reference concepts 105 can be displayed belovv the concept viewing box 104 with an associated classification code 103. The classification code of the similar reference concept 105 can be used as a suggestion for classill ing the selected uncoded concept. After assigning a classification code. a representation 103 of the classification can be provided in the display with the selected uncoded concept. In a further embodiment, the now classified and previously uncoded concept can be added to the set of reference concepts.
Similar reference concepts can be used as suggestions to indicate a need for manual review of the encoded concepts, vv hen review may be unnecessary, and hints for classifying the uncoded concepts, clusters, or spines. Additional information Cary he generated to assist a.
reviewer in making classification decisions for the trncoded concepts, such as a machine-generated confidence level associated with a suggested classification coder as described in common-assigned ITS. Patent Application Serial No. 1211118'44,785- entitled "System and Method for Providing a Classification Suggestion for Concepts," filed on Jule- 277 201 i1, pending, the disclosure of which is incorporated by reference.
The machine-generated suggestion for classification and associated confidence level can be determined by a classifier, FIGURE 8 is a process flow diagrat 110 showing, by ivay of
5 example, a method for classi.lvving uncoded concepts by a classifier for use in the n ethod of FIGURE 2. An uncoded concept is selected from a cluster (block f 11) and compared to a neighborhood of x-similar reference concepts (block 112) to identif those similar reference ccÃrirei [s that are most relevant to the selected u coded conreift. The selected encoded concept can be the same as the encoded concept selected for identifying similar reference concepts or a 10 different uncoded concept. In a further embodiment. a nmrachi.ne-generated suggestion can be provided for a cirrste.r or Spine by selecting and comparum the cluster or spine to a.ueighbo.rhood of .r-reference concepts for the cluster or spine.
The neighborhood of x-similar reference concepts is deternnined separately for each selected uncoded concept and can include one or more similar reference concepts. During 15 neighborhood generation- a value for x-similar reference concepts is first determined automatically or by an individual revieaver. The neighborhood of similar reference concepts can include the reference concepts, which were identified as similar reference concepts according to the method of FIGURE 5. or reference concepts located in one or nmrorr clusters, Such a< the same cluster as the selected tÃrtcoded document or in one or more files, such as an email file. Next, the x-number of similar reference concepts nearest to the selected urtcoded concept are identified.
Finally, the identified x-number of similar reference concepts are provided as the neighborhood for the selected uncoded concept.. In a .furtlrer embodiment, the x-number of similar reference concepts are defined for each classification code, rather than. across all classification codes.
Once generated, the x-number of similar reference concepts in the neighborhood and the selected uncoded concept are analyzed by the classifier to provide a machine-venerated classification suggestion for assigning a classification code (block 113. A confidence level for the machine-g~enerated classification suggestion is also provided (block. 1.1.4).
The machine-generated analysis of the selected r_rncoded concept and x-number of similar reference concepts can be biased on one or more routines performed by the classifier... such as a nearest neighbor classifier. The routines for determinin a suested classification code include a minimum distance classification rheas rre_ also known as closest nei`rh.bor_ mir inmurn average distance classification measure, maximum count classification measure, and distance weighted maximum count classification measure. The minimum distance classification measure for a selected uncode.d concept includes id:entif yin F a neighbor that is the closest distance to the selected uncoded concept and assigning the classification code of the closest neighbor as the suggested classification code for the selected uncoded concept. The closest neighbor is determined by cornp rring the score vectors for the selected encoded concept with each of the x-numbe.r of sinmrilar reference concepts .in the neighborhood as the cos a to dete.r.mine a distance metric.. The distance metrics for the x-number ofsirnilar reference concepts are compared to iefentif v the similar- reference concept closest to the selected uncoded concept as the closest neighbor.
The nunirrrun-r average distance classification measure includes calculating an average distance of the similar reference concepts for each classification code. The classification code of ltt the similar reference concepts having the closest average distance to the selected uncode f concept is assigned as the suggested classification code. The maximum. count classification measure, also known as the voting classification measure, includes counting a number of similar reference concepts for each classification code and assigning a count or "vole" to the similar reference concepts based on the rssigned classification code. The classification code with the highest number of similar reference concepts or "votes" is assigned to the selected uncoded concept as the suggested classification code. The distance weighted maximum count classification measure includes identrfving a count of all similar reference concepts for each classification code and determining a distance between the selected uncoded concept and each of the similar reference concepts. Each count assigned to the similar reference concepts is weighted ?0 based on the distance of the similar reference concept from the selected encoded concept. Tile classification code -, ith the highest count, after consideration of the c eight, :is assigned to the selected encoded concept as the suggested classification code.
The machine-generated suggested classification code is provided for the selected uncoded concept with a confidence level.. which can be presented as an absolute value or a percentage.
Other confidence level measures are possible. The reviewer can use the suggested classification code and confidence level to assi~.n a classification to the selected uncoded concept.
Alternatively, the -NN classifier can automatically assign the swmwsted classification code. In one e.rnbodi.ment, the :a \N classifier only assigns an imcoded concept with the., suggested classification code if the confidence level is above a threshold value, iw.hich can be set by the 3 3t reviewer or the xr l classifier.
;Machine classification can also occur on a cluster or spine level once one or more concepts in the cluster have been classified. For instance, for cluster classification, a Cluster IS
selected and a score vector for the center of the cluster is determined as described above with reference to FIGURE 5, A neighborhood for the selected cluster can be determined based on a distance metric. 'T'he x -number of simian reference concepts that are closest to the cluster center can be selected for inclusion in the neighborhood, as described above. Each concept in the selected cluster is associated Stith i score vector from which the cluster center score vector is generated. The distance is then determined by comparing tle score vector of the. cluster center > with the, score, vector for each of the similar reference concepts to determine an x number of similar reference concepts that are closest to the cluster center. However, other a methods for generating a neighborhood are possible. Once determined, one of the classification routines is applied tot e neig horl ood to de er rr.inee a suggested class if. cation code and confidence Jove]
for the selected cluster. I'lie net; l boyhood of" x rr unal er of reierer ce ccrrrcepts is determined f o a spine b comparing a spine score vector . ith the vector for each similar reference concept to iclenti.f- the nei-bborhood of similar concepts that are the most similar.
In a further embodiment, once the uncoded concepts areassigned a classification code, the nei.vly-classified uncoded concepts can be placed into the concept reference set for use in providin ; classification suggestions for other uncoded concepts.
in yet a further embodiment, each document can be represented by more than one concept.. Accordingly. to determine a classification code for the document, the classification codes for each of the associated concepts can be analy zed and compared for consideration in classifying the document. In one example, a classification code can be deter ir.iried by counting the number of associated concepts for each classification code and then assi=gned the classification code with the most associated concepts. In a further example, one or more of the associated concepts can be weighted and the class] fication code associated with the hi ;hest ti eight of concepts is assigned. Other methods .fo.r determining a classification code for uncodc d documents based on reference concepts are possible.
Although clustering,, and displaying,, relationships has been described above with reference to concepts, other tokens, such as cvord-level or character-leeel n-grants..
raw tennis. and entities.:
are possible.
While the invention has been particular!. shown and described as referenced to the embodiments thereof, those skilled in the art will understand that the foregoing and other changes in form and detail may be made therein without departing from the spirit and scope.
The neighborhood of x-similar reference concepts is deternnined separately for each selected uncoded concept and can include one or more similar reference concepts. During 15 neighborhood generation- a value for x-similar reference concepts is first determined automatically or by an individual revieaver. The neighborhood of similar reference concepts can include the reference concepts, which were identified as similar reference concepts according to the method of FIGURE 5. or reference concepts located in one or nmrorr clusters, Such a< the same cluster as the selected tÃrtcoded document or in one or more files, such as an email file. Next, the x-number of similar reference concepts nearest to the selected urtcoded concept are identified.
Finally, the identified x-number of similar reference concepts are provided as the neighborhood for the selected uncoded concept.. In a .furtlrer embodiment, the x-number of similar reference concepts are defined for each classification code, rather than. across all classification codes.
Once generated, the x-number of similar reference concepts in the neighborhood and the selected uncoded concept are analyzed by the classifier to provide a machine-venerated classification suggestion for assigning a classification code (block 113. A confidence level for the machine-g~enerated classification suggestion is also provided (block. 1.1.4).
The machine-generated analysis of the selected r_rncoded concept and x-number of similar reference concepts can be biased on one or more routines performed by the classifier... such as a nearest neighbor classifier. The routines for determinin a suested classification code include a minimum distance classification rheas rre_ also known as closest nei`rh.bor_ mir inmurn average distance classification measure, maximum count classification measure, and distance weighted maximum count classification measure. The minimum distance classification measure for a selected uncode.d concept includes id:entif yin F a neighbor that is the closest distance to the selected uncoded concept and assigning the classification code of the closest neighbor as the suggested classification code for the selected uncoded concept. The closest neighbor is determined by cornp rring the score vectors for the selected encoded concept with each of the x-numbe.r of sinmrilar reference concepts .in the neighborhood as the cos a to dete.r.mine a distance metric.. The distance metrics for the x-number ofsirnilar reference concepts are compared to iefentif v the similar- reference concept closest to the selected uncoded concept as the closest neighbor.
The nunirrrun-r average distance classification measure includes calculating an average distance of the similar reference concepts for each classification code. The classification code of ltt the similar reference concepts having the closest average distance to the selected uncode f concept is assigned as the suggested classification code. The maximum. count classification measure, also known as the voting classification measure, includes counting a number of similar reference concepts for each classification code and assigning a count or "vole" to the similar reference concepts based on the rssigned classification code. The classification code with the highest number of similar reference concepts or "votes" is assigned to the selected uncoded concept as the suggested classification code. The distance weighted maximum count classification measure includes identrfving a count of all similar reference concepts for each classification code and determining a distance between the selected uncoded concept and each of the similar reference concepts. Each count assigned to the similar reference concepts is weighted ?0 based on the distance of the similar reference concept from the selected encoded concept. Tile classification code -, ith the highest count, after consideration of the c eight, :is assigned to the selected encoded concept as the suggested classification code.
The machine-generated suggested classification code is provided for the selected uncoded concept with a confidence level.. which can be presented as an absolute value or a percentage.
Other confidence level measures are possible. The reviewer can use the suggested classification code and confidence level to assi~.n a classification to the selected uncoded concept.
Alternatively, the -NN classifier can automatically assign the swmwsted classification code. In one e.rnbodi.ment, the :a \N classifier only assigns an imcoded concept with the., suggested classification code if the confidence level is above a threshold value, iw.hich can be set by the 3 3t reviewer or the xr l classifier.
;Machine classification can also occur on a cluster or spine level once one or more concepts in the cluster have been classified. For instance, for cluster classification, a Cluster IS
selected and a score vector for the center of the cluster is determined as described above with reference to FIGURE 5, A neighborhood for the selected cluster can be determined based on a distance metric. 'T'he x -number of simian reference concepts that are closest to the cluster center can be selected for inclusion in the neighborhood, as described above. Each concept in the selected cluster is associated Stith i score vector from which the cluster center score vector is generated. The distance is then determined by comparing tle score vector of the. cluster center > with the, score, vector for each of the similar reference concepts to determine an x number of similar reference concepts that are closest to the cluster center. However, other a methods for generating a neighborhood are possible. Once determined, one of the classification routines is applied tot e neig horl ood to de er rr.inee a suggested class if. cation code and confidence Jove]
for the selected cluster. I'lie net; l boyhood of" x rr unal er of reierer ce ccrrrcepts is determined f o a spine b comparing a spine score vector . ith the vector for each similar reference concept to iclenti.f- the nei-bborhood of similar concepts that are the most similar.
In a further embodiment, once the uncoded concepts areassigned a classification code, the nei.vly-classified uncoded concepts can be placed into the concept reference set for use in providin ; classification suggestions for other uncoded concepts.
in yet a further embodiment, each document can be represented by more than one concept.. Accordingly. to determine a classification code for the document, the classification codes for each of the associated concepts can be analy zed and compared for consideration in classifying the document. In one example, a classification code can be deter ir.iried by counting the number of associated concepts for each classification code and then assi=gned the classification code with the most associated concepts. In a further example, one or more of the associated concepts can be weighted and the class] fication code associated with the hi ;hest ti eight of concepts is assigned. Other methods .fo.r determining a classification code for uncodc d documents based on reference concepts are possible.
Although clustering,, and displaying,, relationships has been described above with reference to concepts, other tokens, such as cvord-level or character-leeel n-grants..
raw tennis. and entities.:
are possible.
While the invention has been particular!. shown and described as referenced to the embodiments thereof, those skilled in the art will understand that the foregoing and other changes in form and detail may be made therein without departing from the spirit and scope.
Claims (20)
1. A method (50) for displaying relationships between concepts (14c, 14d) to provide classification suggestions via nearest neighbor, comprising:
providing reference concepts (14d) each associated with a classification code (96) and a set of uncoded concepts (14c), wherein each of the reference concepts (14d) and the uncoded concepts (14c) comprises one or more nouns extracted from a plurality of documents (14a, 17, 20, 26, 29);
comparing at least one uncoded concept (14c) with the reference concepts (14d) and identifying one or more of the reference concepts (14d) that are similar to the at least one uncoded concept (14c); and depicting relationships between the at least one uncoded concept (14c) and the similar reference concepts (14d) for classifying the at least one uncoded concept (14c), wherein the steps are performed on a suitably programmed computer.
providing reference concepts (14d) each associated with a classification code (96) and a set of uncoded concepts (14c), wherein each of the reference concepts (14d) and the uncoded concepts (14c) comprises one or more nouns extracted from a plurality of documents (14a, 17, 20, 26, 29);
comparing at least one uncoded concept (14c) with the reference concepts (14d) and identifying one or more of the reference concepts (14d) that are similar to the at least one uncoded concept (14c); and depicting relationships between the at least one uncoded concept (14c) and the similar reference concepts (14d) for classifying the at least one uncoded concept (14c), wherein the steps are performed on a suitably programmed computer.
2. A method (50) according to Claim 1, further comprising:
classifying the at least one uncoded concept by assigning a classification code (96) based on the relationships between the at least one uncoded concept and the similar reference concepts (14d).
classifying the at least one uncoded concept by assigning a classification code (96) based on the relationships between the at least one uncoded concept and the similar reference concepts (14d).
3. A method (50) according to Claim 2, further comprising:
adding the classified at least one uncoded concept to the reference concepts (14d).
adding the classified at least one uncoded concept to the reference concepts (14d).
4. A method (50) according to Claim 2, further comprising:
providing a confidence level for the classification code (96) of the at least one uncoded concept.
providing a confidence level for the classification code (96) of the at least one uncoded concept.
5. A method (50) according to Claim 2, further comprising:
identifying those documents (14a, 17, 20, 26, 29) associated with the classified at least one uncoded concept; and assigning the classification code (96) for the classified at least one uncoded concept to one or more of the associated documents (14a, 17, 20, 26, 29).
identifying those documents (14a, 17, 20, 26, 29) associated with the classified at least one uncoded concept; and assigning the classification code (96) for the classified at least one uncoded concept to one or more of the associated documents (14a, 17, 20, 26, 29).
6. A method (50) according to Claim 5, wherein the documents (14a, 17, 20, 26, 29) are identified using a matrix comprising a mapping of concepts and related documents (14a, 17, 20, 26, 29).
7. A method (50) according to Claim 1, further comprising:
generating the reference concepts (14d) from a set of concepts, comprising at least one of:
identifying the concepts that are dissimilar from each other concept in the set of concepts and assigning the classification code (96) to each of the dissimilar concepts, as the reference concepts (14d); and grouping the set of concepts into clusters (92), selecting one or more of the concepts in at least one cluster, and assigning the classification code (96) to each of the selected concepts, as the reference concepts (14d).
generating the reference concepts (14d) from a set of concepts, comprising at least one of:
identifying the concepts that are dissimilar from each other concept in the set of concepts and assigning the classification code (96) to each of the dissimilar concepts, as the reference concepts (14d); and grouping the set of concepts into clusters (92), selecting one or more of the concepts in at least one cluster, and assigning the classification code (96) to each of the selected concepts, as the reference concepts (14d).
8. A method (50) according to Claim 1, further comprising:
determining the similar reference concepts (14d), comprising:
forming a score vector for each uncoded concept and each reference concept; and calculating a similarity metric by comparing the score vectors for the at least one uncoded concept and each of the reference concepts (14d);
and selecting the reference concepts (14d) with the highest similarity metrics as the similar reference concepts (14d).
determining the similar reference concepts (14d), comprising:
forming a score vector for each uncoded concept and each reference concept; and calculating a similarity metric by comparing the score vectors for the at least one uncoded concept and each of the reference concepts (14d);
and selecting the reference concepts (14d) with the highest similarity metrics as the similar reference concepts (14d).
9. A method (50) according to Claim 1, further comprising:
determining the similar reference concepts (14d), comprising:
determining a measure of similarity between the at least one uncoded concept and each of the reference concepts (14d) based on the comparison;
applying a threshold to the measures of similarity; and selecting those reference concepts (14d) that satisfy the threshold as the similar reference concepts (14d).
determining the similar reference concepts (14d), comprising:
determining a measure of similarity between the at least one uncoded concept and each of the reference concepts (14d) based on the comparison;
applying a threshold to the measures of similarity; and selecting those reference concepts (14d) that satisfy the threshold as the similar reference concepts (14d).
10. A method (50) according to Claim 1, further comprising:
clustering the uncoded concepts (14c) and displaying the clusters (92);
and displaying the similar reference concepts (14d) in a list adjacent to the clusters (92).
clustering the uncoded concepts (14c) and displaying the clusters (92);
and displaying the similar reference concepts (14d) in a list adjacent to the clusters (92).
11. A system (10) for displaying relationships between concepts to provide classification suggestions via nearest neighbor, comprising:
a database to maintain reference concepts (14d) each associated with a classification code (96) and a set of uncoded concepts (14c), wherein each of the reference concepts (14d) and the uncoded concepts (14c) comprises one or more nouns extracted from a plurality of documents (14a, 17, 20, 26, 29);
a similarity module to compare at least one uncoded concept with the reference concepts (14d) and to identify one or more of the reference concepts (14d) that are similar to the at least one uncoded concept; and a display to depict relationships between the at least one uncoded concept and the similar reference concepts (14d) for classifying the at least one uncoded concept.
a database to maintain reference concepts (14d) each associated with a classification code (96) and a set of uncoded concepts (14c), wherein each of the reference concepts (14d) and the uncoded concepts (14c) comprises one or more nouns extracted from a plurality of documents (14a, 17, 20, 26, 29);
a similarity module to compare at least one uncoded concept with the reference concepts (14d) and to identify one or more of the reference concepts (14d) that are similar to the at least one uncoded concept; and a display to depict relationships between the at least one uncoded concept and the similar reference concepts (14d) for classifying the at least one uncoded concept.
12. A system (10) according to Claim 11, further comprising:
a classification module to classify the at least one uncoded concept by assigning a classification code (96) based on the relationships between the at least one uncoded concept and the similar reference concepts (14d).
a classification module to classify the at least one uncoded concept by assigning a classification code (96) based on the relationships between the at least one uncoded concept and the similar reference concepts (14d).
13. A system (10) according to Claim 12, further comprising:
a reference module to add the classified at least one uncoded concept to the reference concepts (14d).
a reference module to add the classified at least one uncoded concept to the reference concepts (14d).
14. A system (10) according to Claim 12, wherein the classification module provides a confidence level for the classification code (96) of the at least one uncoded concept.
15. A system (10) according to Claim 12, further comprising:
a document classification module to identify those documents (14a, 17, 20, 26, 29) associated with the classified at least one uncoded concept and to assign the classification code (96) for the classified at least one uncoded concept to one or more of the associated documents (14a, 17, 20, 26, 29)
a document classification module to identify those documents (14a, 17, 20, 26, 29) associated with the classified at least one uncoded concept and to assign the classification code (96) for the classified at least one uncoded concept to one or more of the associated documents (14a, 17, 20, 26, 29)
16. A system (10) according to Claim 15, wherein the documents (14a, 17, 20, 26, 29) are identified using a matrix comprising a mapping of concepts and related documents (14a, 17, 20, 26, 29).
17. A system (10) according to Claim 11, further comprising:
a reference set module to generate the reference concepts (14d) from a set of concepts, comprising at least one of-a comparison module to identify the concepts that are dissimilar from each other concept in the concept set and to assign the classification code (96) to each of the dissimilar concepts, as the reference concepts (14d); and a reference clustering module to group the set of concepts into one or more clusters (92), to select one or more of the concepts in at least one cluster, and to assign the classification code (96) to each of the selected concepts, as the reference concepts (14d).
a reference set module to generate the reference concepts (14d) from a set of concepts, comprising at least one of-a comparison module to identify the concepts that are dissimilar from each other concept in the concept set and to assign the classification code (96) to each of the dissimilar concepts, as the reference concepts (14d); and a reference clustering module to group the set of concepts into one or more clusters (92), to select one or more of the concepts in at least one cluster, and to assign the classification code (96) to each of the selected concepts, as the reference concepts (14d).
18. A system (10) according to Claim 11, further comprising:
a concept similarity module to determine the similar reference concepts (14d), comprising:
a vector module to form a score vector for each uncoded concept and each reference concept; and a similarity measurement module to calculate a similarity metric by comparing the score vectors for the at least one uncoded concept and each of the reference concepts (14d) and to select the reference concepts (14d) with the highest similarity metrics as the similar reference concepts (14d).
a concept similarity module to determine the similar reference concepts (14d), comprising:
a vector module to form a score vector for each uncoded concept and each reference concept; and a similarity measurement module to calculate a similarity metric by comparing the score vectors for the at least one uncoded concept and each of the reference concepts (14d) and to select the reference concepts (14d) with the highest similarity metrics as the similar reference concepts (14d).
19. A system (10) according to Claim 11, further comprising:
a concept similarity module to determine the similar reference concepts (14d), comprising:
a similarity measurement module to determine a measure of similarity between the at least one uncoded concept and each of the reference concepts (14d) based on the comparison; and a threshold module to apply a threshold to the measures of similarity and to select those reference concepts (14d) that satisfy the threshold as the similar reference concepts (14d).
a concept similarity module to determine the similar reference concepts (14d), comprising:
a similarity measurement module to determine a measure of similarity between the at least one uncoded concept and each of the reference concepts (14d) based on the comparison; and a threshold module to apply a threshold to the measures of similarity and to select those reference concepts (14d) that satisfy the threshold as the similar reference concepts (14d).
20. A system (10) according to Claim 11, further comprising:
a clustering module to cluster the uncoded concepts (14c); and the display to present the clusters (92) and the similar reference concepts (14d) in a list adjacent to the clusters (92).
a clustering module to cluster the uncoded concepts (14c); and the display to present the clusters (92) and the similar reference concepts (14d) in a list adjacent to the clusters (92).
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