WO2004001623A2 - Constructing a translation lexicon from comparable, non-parallel corpora - Google Patents
Constructing a translation lexicon from comparable, non-parallel corpora Download PDFInfo
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- WO2004001623A2 WO2004001623A2 PCT/US2003/009573 US0309573W WO2004001623A2 WO 2004001623 A2 WO2004001623 A2 WO 2004001623A2 US 0309573 W US0309573 W US 0309573W WO 2004001623 A2 WO2004001623 A2 WO 2004001623A2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/237—Lexical tools
- G06F40/242—Dictionaries
Definitions
- Machine translation concerns the automatic translation of natural language sentences from a first language (e.g., French) into another language (e.g., English). Systems that perform MT techniques are said to "decode" the source language into the target language.
- SMT statistical machine translation
- a system may be able to build a translation lexicon from comparable, non-parallel corpora.
- the system may identify all identically spelled words in the' corpora and use these as a seed lexicon for other processes based on clues indicating possible translations.
- a system may align text segments in comparable, non-parallel corpora, matching strings in the corpora, and using the matched strings to build a parallel corpus.
- the system may build a Bilingual Suffix Tree (BST) and traverse edges of the BST to identify matched strings.
- the BST may also identify potential translations based on words in the corpora between matched strings.
- Figure 1 is a block diagram of a system for building a translation lexicon according to an embodiment.
- Figure 2 is a flowchart describing a method for building a translation lexicon from non-parallel corpora.
- Figure 3 is a table showing results of an experiment utilizing the system of Figure 1.
- Figure 4 is a block diagram of a system for building a translation lexicon according to another embodiment.
- Figure 5 is a suffix tree.
- Figure 6 is a Generalized Suffix Tree (GST) .
- Figure 7 is a Bilingual Suffix Tree (BST) .
- Figure 8 is a portion of a BST showing example alignments .
- Figure 9 are portions of a BST describing left and right alignments.
- Figure 10 is psuedocode describing an algorithm for learning translations of unknown words.
- Figure 1 shows a system 100 for building a translation lexicon 105 according to an embodiment.
- the system may use non-parallel monolingual corpora 110, 115 in two languages to automatically generate one-to-one mapping of words in the two languages.
- the two monolingual corpora should be in a fairly comparable domain.
- an English-German translation lexicon was generated from a 1990- 1992 Wall Street Journal corpus on the English side and a 1995-1996 German news wire (DPA) on the German side.
- DPA German news wire
- Both corpora are news sources in the general sense. However, they span different time periods and have a different orientation: the World Street Journal covers mostly business news, the German news wire mostly German politics.
- the system 100 may use clues to find translations of words in the monolingual corpora.
- the first clue considered may be the existence of identical words in the two corpora. Due to cultural exchange, a large number of words that originate in one language may be adopted by others. Recently, this phenomenon can be seen with words such as "Internet” or "Aids". These terms may be adopted verbatim or changed by well-established rules. For instance, "immigration” (German and English) has the Portuguese translation "immigragao", as many words ending in -tion have translations with the same spelling except for the ending changed to -cao.
- Figure 2 shows a flowchart describing a method 200 for building a translation lexicon from non-parallel corpora.
- a word comparator 120 may be used to collect pairs of identical words (block 205) . In the English-German implementation described above, 977 identical words were found. When checked against a benchmark lexicon, the mappings were found to be 88% correct.
- word comparator 120 may restrict the word length to be able to increase the accuracy of the collected word pairs. For instance, by relying only on words at least of length six, 622 word pairs were collected with 96% accuracy.
- the identified identically spelled word pairs may be used as a seed lexicon 130 (block 210) .
- a lexicon builder 125 may expand the seed lexicon into the larger translation lexicon 105 by applying rules based on clues which indicate probable translations. The lexicon builder 125 may use seed lexicon to bootstrap these methods, using the word pairs in the seed lexicon as correct translations.
- the lexicon builder 125 extracts potential translation word pairs based on one or more clues. These clues may include similar spelling, similar context, preserving word similarity, and word frequency. [0025] When words are adopted into another language, their spelling might change slightly in a manner that cannot be simply generalized in a rule, e.g., "website” and “Webseite”. This is even more the case for words that can be traced back to common language roots, such as "friend” and "Freund", or "president” and “Prasident”. Still, these words, often called “cognates”, maintain a very similar spelling. This can be defined as differing in very few letters. This measurement can be formalized as the number of letters common in sequence between the two words, divided by the length of the longer word.
- the example word pair "friend” and “freund” shares 5 letters (fr-e-nd), and both words have length 6, hence their spelling similarity is 5/6, or 0.83. This measurement may be referred to as the "longest common subsequence ratio.”
- the lexicon builder 125 may measure the spelling similarity between every German and English word, and sort possible word pairs accordingly. This may be done in a greedy fashion, i.e., once a word is assigned to a word pair, the lexicon builder 125 does not look for another match.
- the lexicon builder 125 may count how often another word occurs in the same sentence as the target word. The counts may then be normalized by a using the tf/idf method, which is often used in information retrieval.
- the seed lexicon may be used to construct context vectors that contain information about how a new unmapped word co-occurs with the seed words. This vector can be translated into the other language, since we already know the translations of the seed words are already known.
- the lexicon builder 125 can search for the best matching context vector in the target language, and decide upon the corresponding word to construct a word mapping.
- the lexicon builder 125 may compute all possible word, or context vector, matches. The best word matches may be collected in a greedy fashion.
- Another clue is based on the assumption that pairs of words that are similar in one language should have translations that are similar in the other language. For instance, Wednesday is similar to Thursday as Mittwoch is similar to Donnerstag. Two words may be defined as similar if they occur in a similar context, which is the case for Wednesday and Thursday.
- the context vector for each word in the lexicon may consist of co-occurrence counts in respect to a number of peripheral tokens (basically, the most frequent words) . These counts may be collected for each position in an n-word window around the word in focus.
- the Spearman rank order correlation may be applied. For each position, the tokens are compared in frequency and the frequency count is replaced by the frequency rank, e.g., the most frequent token count is replaced with 1 and the least frequent by n .
- the result is a matrix with similarity scores between all German words, and a second matrix with similarity scores between all English words.
- the lexicon builder 125 may look up its similarity scores to seed words, thus creating a similarity vector. Such a vector can be translated into the other language. The translated vector can be compared to other vectors in the second language.
- the lexicon builder 125 may perform a greedy search for the best matching similarity vectors sand add the corresponding words to the lexicon.
- Each of the clues provides a matching score between two words (block 220), e.g., a German word and an English word. The likelihood of these two words being actual translations of each other may correlate to these scores.
- the lexicon builder 125 may employ a greedy search to determine the best set of lexicon entries based on these scores (block 225) . First, the lexicon builder 125 searches for the highest score for any word pair.
- the lexicon builder 125 may combine different clues by adding up the matching scores.
- the scores can be weighted. For example, when using the spelling clue in combination with others, it may be useful to define a cutoff. If two words agree in 30% of their letters, this is generally as bad as if they do not agree in any, i.e., the agreements are purely coincidental .
- Figure 3 shows results of the English-German implementation. "Entries” indicate the number of correct lexicon entries that were added to a seed lexicon of 1337 identically spelled words, and “Corpus” indicates how well the resulting translation lexicon performs compared to the actual word-level translations in a parallel corpus.
- Figure 4 shows a system 400 for building a translation lexicon according to another embodiment.
- the system 400 may also be used to build parallel corpora from comparable corpora.
- the system 400 may identify parts of the texts which can be aligned (i.e., are mutual translations of each other according to the lexicon).
- the parts can be arbitrarily long, i.e., the system 400 may align sequences of a few words rather than or in addition to whole sentences or whole phrases.
- the system 400 may generate a parallel corpus 420 and identify translations 425 of words from the source language which are not in the lexicon.
- the system 400 may search the corpora for cases similar to this example. [0044]
- the system 400 may use a suffix tree data structure in order to identify the alignments.
- the suffix tree of a string uniquely encodes all the suffixes of that string (and thus, implicitly, all its substrings too) .
- the system 400 may first build such a tree of the target language corpus, and then add to each substrings all the substrings from the source language corpus that align to it. The next step is to identify unknown target language words that are surrounded by aligned substrings.
- the source language word that corresponds to the "well-aligned" unknown is considered to be a possible translation.
- a suffix tree stores in linear space all suffixes of a given string.
- Such succinct encoding exposes the internal structure of the string, providing efficient (usually linear- time) solutions for many complex string problems, such as exact and approximate string matching, finding the longest common substring of multiple strings, and string compression.
- a suffix tree for a string S of length N has the following properties: each edge of the tree is labeled by a nonempty substring of S; each internal node other than the root has at least two children; no two edges out of a node can have edge-labels beginning with the same character/word; and (the key feature of the tree) there is a one-to-one correspondence between all suffixes of S and paths in the tree from the root to the leaves.
- Figure 5 shows the suffix tree 500 of string xyzyxzy.
- Each monolingual corpus given as input to the system 400 may be divided into a set of sentences.
- the system 400 may use a variant of suffix trees that works with sets of strings, namely Generalized Suffix Trees (GST) .
- GST Generalized Suffix Trees
- each path from the root to a leaf represents a suffix in one or more strings from the set.
- a conceptually easy way to build such a tree is to start by building a regular suffix tree for the first sentence in the corpus, and then for each of the other sentences to take their suffixes one by one and add them to the tree (if they are not already in it) .
- Figure 6 shows the GST 600 for a corpus of two sentences.
- a Bilingual Suffix Tree is the result of matching a source language GST against a target language GST.
- Two strings i.e., sequences of words
- all paths that correspond to an exhaustive traversal of one of the trees (the source tree) are traversed in the other (the target tree), until a mismatch occurs.
- the target tree is augmented with information about the alignments between its paths and those of the source, thus becoming a bilingual suffix tree.
- Figure 7 shows two corpora 705, 710, a bilingual lexicon 715, and the corresponding BST 720. Edges drawn with dotted lines mark ends of alignment paths through the tree. Their labels are (unaligned) continuations of the source language substrings from the respective paths.
- BSTs are constructed to encode alignment information, therefore the extraction of parallel phrases amounts to a simple depth-first traversal of the tree.
- Figure 8 shows some alignments we can extract from the BST in Figure 7, a portion of which is shown in Figure 8.
- edge labels in a BST there are three types of edge labels in a BST: only target language sequences (e.g., xzy) , pairs of target and source language sequences (y:b followed by z:c) and only source language words (b or c) .
- target language sequences e.g., xzy
- pairs of target and source language sequences y:b followed by z:c
- source language words b or c
- Figure 9(i) shows a branch 905 of the BST corresponding to the comparable corpus in the same figure.
- the path defined by the bold edges shows that sequences xyz and abc are aligned, and diverge (i.e., have a mismatch) at characters y and d respectively. This may be taken as a weak indication that d and y are translations of each other. This indication would become stronger if, for example, the sequences following d and y in the two corpora would also be aligned.
- the system 400 uses two simple heuristics: length and word content.
- the left and right context together must contain at least three words, one of which must be an open-class word, e.g., a noun, verb, adjective, or adverb, classes which can have new words added to them.
- the translation candidate must also be an open-class word.
- the algorithm 1000 for learning translations of unknown words is summarized in Figure 10. An advantage of the algorithm over previous approaches is that we do not provide as input to the algorithm a list of unknown words. Instead, the system automatically learns from the corpus both the unknown words and their translation, upon discovery of appropriate context alignments.
- the system 400 was tested on an English-French comparable corpus, of approximately 1.3 million words -50.000 sentences for each language. It was obtained by taking two non-parallel, nonaligned segments from the Hansard corpus.
- the Hansard Corpus includes parallel texts in English and Canadian French, drawn from official records of the proceedings of the Canadian Parliament. A small bilingual lexicon of 6,900 entries was built using 5,000 sentences pairs (150,000 words for each language).
- the parallel corpus was taken from the Proceedings of the European Parliament (EuroParl) Note that the parallel corpus belongs to a different domain than the comparable corpus. Also the parallel corpus is extremely small. For low density languages, such a corpus can be built manually.
- the algorithm 1000 When given as input the comparable corpora described above and the bilingual lexicon of 6,900 entries, the algorithm 1000 found 33,926 parallel sequences, with length between three and seven words. Out of 100 randomly selected sequences, 95% were judged to be correct. [0060] The system also found translations for thirty unknown French words. Of these, nine were correct, which means a precision of 30%.
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WO2004001623A3 (en) | 2004-07-15 |
US20030204400A1 (en) | 2003-10-30 |
US20100042398A1 (en) | 2010-02-18 |
US7620538B2 (en) | 2009-11-17 |
AU2003269808A1 (en) | 2004-01-06 |
US8234106B2 (en) | 2012-07-31 |
AU2003269808A8 (en) | 2004-01-06 |
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