CA2534273A1 - Performing efficient document scoring and clustering - Google Patents

Performing efficient document scoring and clustering Download PDF

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Publication number
CA2534273A1
CA2534273A1 CA002534273A CA2534273A CA2534273A1 CA 2534273 A1 CA2534273 A1 CA 2534273A1 CA 002534273 A CA002534273 A CA 002534273A CA 2534273 A CA2534273 A CA 2534273A CA 2534273 A1 CA2534273 A1 CA 2534273A1
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Prior art keywords
concept
score
weight
documents
document
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CA002534273A
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French (fr)
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CA2534273C (en
Inventor
Kenji Kawai
Lynne Marie Evans
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FTI Technology LLC
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Attenex Corporation
Kenji Kawai
Lynne Marie Evans
Fti Technology Llc
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Publication of CA2534273A1 publication Critical patent/CA2534273A1/en
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Classifications

    • 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
    • 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
    • G06F16/353—Clustering; Classification into predefined classes
    • 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
    • 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
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00—Data processing: database and file management or data structures
    • Y10S707/99931—Database or file accessing
    • Y10S707/99933—Query processing, i.e. searching
    • Y10S707/99934—Query formulation, input preparation, or translation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00—Data processing: database and file management or data structures
    • Y10S707/99931—Database or file accessing
    • Y10S707/99937—Sorting

Abstract

A system (10) and method (80) for grouping clusters (58) of semantically scored documents (14) is presented. A score (52) assigned to at least one concept (49) extracted from a plurality of documents (14) is determined based on at least one of a frequency of occurrence (53) of the at least one concept (49) within at least one such document (14), a concept weight (54), a structural weight (55), and a corpus weight (56). Clusters (58) of the documents (14) are formed by applying the score (52) for the at least one concept (49) to a best fit criterion for each such document (14).

Claims (17)

1. A system (10) for grouping clusters (58) of semantically scored documents (14), comprising:
a scoring module (42) determining a score (52) assigned to at least one concept (49) extracted from a plurality of documents (14) based on at least one of a frequency of occurrence (53) of the at least one concept (49) within at least one such document (14), a concept weight (54), a structural weight (55), and a corpus weight (56); and a clustering module (43) forming clusters (58) of the documents (14) by applying the score (52) for the at least one concept (49) to a best fit criterion for each such document (14).
2. A system (10) according to Claim 1, further comprising:
the scoring module (42) calculating the score (52) as a function of a summation of at least one of the frequency of occurrence (53), the concept weight (54), the structural weight (55), and the corpus weight (56) of the at least one concept (49).
3. A system (10) according to Claim 2, further comprising:
a compression module (31) compressing the score (52) through logarithmic compression.
4. A system (10) according to Claim 1, further comprising:
the scoring module (42) calculating the concept weight (54) as a function of a number of terms (50) comprising the at least one concept (49).
5. A system (10) according to Claim 1, further comprising:
the scoring module (42) calculating the structural weight (55) as a function of a location (125) of the at least one concept (49) within the at least one such document (14).
6. A system (10) according to Claim 1, further comprising:
the scoring module (42) calculating the corpus weight (56) as a function of a reference count (152) of the at least one concept (49) over the plurality of documents (14).
7. A system (10) according to Claim 1, further comprising:
the scoring module (42) forming the score (52) assigned to the at least one concept (49) to a normalized score vector (57) for each such document (14), determining a similarity between the normalized score vector (57) for each such document (14) as an inner product of each normalized score vector (57), and applying the similarity to the best fit criterion.
8. A system (10) according to Claim 1, further comprising:
the clustering module (43) evaluating a set of candidate seed documents (14) selected from the plurality of documents (14), identifying a set of seed documents (14) by applying the score (52) for the at least one concept (49) to a best fit criterion for each such candidate seed document (14), and basing the best fit criterion on the score (52) of each such seed document (14).
9. A method (80) for grouping clusters (58) of semantically scored documents (14), comprising:
determining (140) a score (52) assigned to at least one concept (49) extracted from a plurality of documents (14) based on at least one of a frequency of occurrence (53) of the at least one concept (49) within at least one such document (14), a concept weight (54), a structural weight (55), and a corpus weight (56); and forming (160) clusters (58) of the documents (14) by applying the score (52) for the at least one concept (49) to a best fit criterion for each such document (14)s.
10. A method (80) according to Claim 9, further comprising:
calculating (145) the score (52) as a function of a summation of at least one of the frequency of occurrence (53), the concept weight (54), the structural weight (55), and the corpus weight (56) of the at least one concept (49).
11. A method (80) according to Claim 10, further comprising:
compressing (146) the score (52) through logarithmic compression.
12. A method (80) according to Claim 9, further comprising:

calculating (142) the concept weight (54) as a function of a number of terms (50) comprising the at least one concept (49).
13. A method (80) according to Claim 9, further comprising:
calculating (143) the structural weight (55) as a function of a location (125) of the at least one concept (49) within the at least one such document (14).
14. A method (80) according to Claim 9, further comprising:
calculating (144) the corpus weight (56) as a function of a reference count (152) of the at least one concept (49) over the plurality of documents (14).
15. A method (80) according to Claim 9, further comprising:
forming the score (52) assigned to the at least one concept (49) to a normalized score vector (57) for each such document (14);
determining (165) a similarity between the normalized score vector (57) for each such document (14) as an inner product of each normalized score vector (57); and applying (177) the similarity to the best fit criterion.
16. A method (80) according to Claim 9, further comprising:
evaluating (161) a set of candidate seed documents (14) selected from the plurality of documents (14);
identifying (166) a set of seed documents (14) by applying the score (52) for the at least one concept (49) to a best fit criterion for each such candidate seed document (14); and basing (177) the best fit criterion on the score (52) of each such seed document (14).
17. A computer-readable storage medium (11) holding code for performing the method (80) of Claim 9.
CA2534273A 2003-07-25 2004-07-23 Performing efficient document scoring and clustering Active CA2534273C (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US10/626,984 2003-07-25
US10/626,984 US7610313B2 (en) 2003-07-25 2003-07-25 System and method for performing efficient document scoring and clustering
PCT/US2004/023955 WO2005013152A1 (en) 2003-07-25 2004-07-23 Performing efficient document scoring and clustering

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CA2534273A1 true CA2534273A1 (en) 2005-02-10
CA2534273C CA2534273C (en) 2013-08-20

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US (3) US7610313B2 (en)
EP (1) EP1652119A1 (en)
CA (1) CA2534273C (en)
WO (1) WO2005013152A1 (en)

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US20050022106A1 (en) 2005-01-27
US20140122495A1 (en) 2014-05-01
US7610313B2 (en) 2009-10-27
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