Gale, W. A., & Church, K. W. (1991a). "Identifying word correspondences in parallel texts". Fourth DARPA Workshop on Speech and Natural language, Asilomar, California.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....that can be expressed by symbols. The problem we need to address is how to identify symbol correspondence. Given any pair of symbols in which represents a visual cluster and represents an audio cluster, we 28 can measure the association between by making use of mutual information [Gale and Church, 1991]. a like statistic, seems to be a good measurement of correlation: 8) is the probability of and co occurrence, is not in close temporal proximity; is not in close temporal proximity. is the probability that neither nor ....

Gale, W. and Church, K. (1991). Identifying word correspondences in parallel texts. In Proceedings of the DDARPA SNL Workshop.

....pairs involving S or T in the same sentence pair. An alignment filter is based on the relative positions of S and T in their respective texts[Dag93] The decision procedure used to select lexicon entries from the multiset of candidate translation pairs is a variation of the method presented in [Gal91a]. Dun93] found binomial log likelihood ratios to be relatively accurate when dealing with rare tokens. This statistic was used to estimate dependencies between all co occuring (source word, target word) pairs. For each source word S, target words were ranked by their dependence with S. The top N ....

W. Gale & K. W. Church, "Identifying Word Correspondences in Parallel Texts," Pro- ceedings of the DARPA $NL Workshop, 1991.

....which is not yet used by the program. 6 Related Research : In this section we compare our work with two other methods reported on in the literature. In section 6.1 we compare our work to work discussed in [Gaussier et al. 1992] which is based on mutual information. Section 6. 2 discusses [Gale and Church, 1991a] which is based on the 42 statistic. sit is conceivable to partly automate the acquisition of the necessary lexical knowledge, viz. determining which nouns are likely to take PP complements, but our corpus is too small for this type of knowledge acquisition. In fact, it turned out to be better ....

....expense of recall. The position sensitive result is comparable to the 90 row in table 7. Figure 9: Phrase based methods using mutfial infor mation Position I Filter I Recall [Precision no no 66 (98 ) 25 yes no 66 (98 ) 58 no yes 55 (82 ) 38 yes yes 40 (59 ) 89 6. 2 The b 2 method In [Gale and Church, 1991a] another association measure is used, viz. b 2, a X2 1ike statistic. In the following formula, assume a is the co occurrence frequency of a source language term sl and a target language term tl, b the frequency of sl minus a, c the frequency of II minus a, and d the number of regions containing ....

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W. Gale and K. Church. Identifying word correspondences in parallel texts. In dth Darpa Workshop on Speech and Natural Language, pages 152-157, 1991.

....The sub script i denotes the i th alignment of sentences in both languages. A word sequence in Ei is defined here as the correspondence of another sequence in Fi if the words of one sequence are considered to represent the words in the other. Single word correspondences have been investi gated [Gale and Church, 1991a] using a statistic operating on contingency tables. An algorithm for producing collocational correspondences has also been described [Smadja, 1992] The algorithm in volves several steps. English collocations are first extracted from the English side of the corpus. Instances of the English ....

....operation. An arbitrarily large corpus can be accommodated by segmenting it appropriately. The algorithm described here is an instance of a general approach to statistical estimation, rep resented by the EM algorithm [Dempster et al. 1977] In contrast to reservations that have been expressed [Gale and Church, 1991a] about us ing the EM algorithm to provide word correspondences, there have been no indications that prohibitive amounts of memory might be required, or that the approach lacks robustness. Unlike the other methods that have been mentioned, the approach has the capability to accommodate more ....

W. A. Gale and K. W. Church. Identifying word correspondences in parallel texts. In Proceedin#s of the Fourth DARPA Speech and Natural an#ua#e Workshop, pages 152-157, Pacific Grove, CA., February 1991. Morgan Kaufmann.

....table of co occurrence frequencies of tx and ty below, bilingual term correspondences can be estimated according to the statistical measures such as the mutual information, the b 2 statistic, the dice coefficient, the log likelihood ratio, and also certain types of their extensions (e. g, [Gale91, Kumano94, Haruno96a, Smadja96, Kitamura96, Melamed00]) tx freq(tx, t) freq(tx, t) tx freq(tx,t) freq(tx,t) Matsumoto97] also proposed a method for acquiring translation rules of machine translation systems from the results of syntactic structure level alignment [Matsumoto93] of parallel sentences. Detailed introductory descriptions regarding ....

Gale, W. and Church, K.: Identifying Word Correspondences in Parallel Texts, Proc. th DARPA Speech and Natural Language Workshop, pp. 152 157 (1991).

....system, a disambiguation of the English translation candidates is performed, by selecting the best English term, equivalent to each French query term, by applying a statistical method based on the co occurrence frequency. For the purpose of this study, we decided to use the mutual information [2], which is defined as follows: MI (W 1 ,W 2 ) Log 2 Where N is the size of the corpus, f(w)is the number of times the word w occurs in the corpus and f(w 1, w 2 ) is the number of times both w 1 and w 2 occur together in a sentence bead. 3 Query Expansion in Cross Language Information ....

....with that terminology, in Cross Language Information Retrieval. Terms Extraction for a Feedback Loop According to previous researches [1] 4] query expansion before and after translation improves the effectiveness of an information retrieval. In our case, we used the mutual information [2] to select and add those terms, which occur most often with the original query terms. Previous results showed that results based on the mutual information are significantly worst that those based on the log likelihood ratio or chi square test or modified dice coefficient [3] For an efficient use ....

Gale, W. A. and Church, K. : "Identifying word correspondences in parallel texts". Proceedings of the 4 th DARPA Speech and Natural Language Workshop, (1991). P.152-157.

....is a translation of the Japanese word J , then one would expect that when the Japanese word J is present in a Japanese sentence, its English translation E would also appear in the paired English sentence. A number of statical measures, such as mutual information, likelihood ratio test based [9, 8], have been developed to compute the association significance between two events. We used the likelihood ratio test based measure developed by Dunning [8] to compute the association strength between a pair of Japanese English words. From the aligned sentences, we constructed a contingency table ....

W. A. Gale and K. W. Church. Identifying word correspondences in parallel texts. In Proceedings of the Fourth DARPA Speech and Natual Language Workshop, pages 152--157, Pacific Grove, CA, 1991.

....We currently use two cost functions. AlshawiBangaloreDouglasFinal.tex; 1 09 1999; 17:19; p.8 9 Figure 5. Hierarchical alignment of I want to make a collect call with quiero hacer una llamada de cobrar The first, and primary, cost function is the OE correlation measure (cf the use of OE 2 in Gale and Church, 1991) computed as follows: OE = bc Gamma ad) p (a b) c d) a c) b d) where a = nw Gamma nw;v b = nw;v c = N Gamma n v Gamma nw nw;v d = n v Gamma nw;v N is the total number of bitexts, n v the number of bitexts in which v appears in the target, nw the number of bitexts in which ....

Gale, W. and K. Church: 1991, `Identifying word correspondences in parallel texts'. In: Proceedings of the Fourth DARPA Speech and Natural Language Processing Workshop. Pacific Grove, California, pp. 152--157.

....statistical function needs to indicate the strength of co occurrence correlation between source and target words, which we assume is indicative of carrying the same semantic content. Our preferred choice of statistical measure for assigning the costs is the so called OE correlation measure (Gale and Church, 1991). We apply this statistic to co occurrence of the source word with all its possible translations in the dataset examples. We have found that, at least for our data, this measure leads to better performance than the use of the log probabilities of target words given source words (cf Brown et al. ....

Gale, W.A. and K.W. Church. 1991. Identifying word correspondences in parallel texts. In Proceedings of the Fourth DARPA Speech and Natural Language Processing Workshop, pages 152--157, Pacific Grove, California.

....be zero, one, or several target language words. The assignment of translation pairing costs (effectively a statistical bilingual dictionary) may be done using various statistical measures. Our preferred choice of statistical measure for assigning the costs is the so called f correlation measure ([6]) We apply this statistic to co occurrence of the source word with all its possible translations in the dataset examples. We have found that, at least for our data, this measure leads to better performance than the use of the log probabilities of target subsequences given source words (cf [4] ....

W.A. Gale and K.W. Church. Identifying word correspondences in parallel texts. In Proceedings of the Fourth DARPA Speech and Natural Language Processing Workshop, pages 152-157, Pacific Grove, California. 1991.

....we do not. Finally, his search for word meanings is most analogous to a version space search, while ours is a greedy search. This work also has ties to the work on automatic construction of translation lexicons (Wu Xia 1995; Melamed 1995; Kumano H 1994; Catizone, Russell, Warwick 1993; Gale Churck 1991). While most of these methods also compute association scores between pairs (in their case, word word pairs) and use a greedy algorithm to choose the best translation(s) for each word, they do not take advantage of the constraints between pairs. One exception is Melamed (1996) however, his ....

Gale, W., and Churck, K. 1991. Identifying word correspondences in parallel texts. In Proceedings of the Fourth DARPA Speech and Natural Language Workshop.

.... Assistance (UC DATA) University of California at Berkeley, CA 94720 gey ucdata.berkeley.edu Abstract Recent years have seen active research in the statistical derivation of bilingual lexicons from bilingual corpora within the machine translation and computational linguistic communities [1, 4, 6, 7, 12, 13, 15, 17]. Bilingual lexicons have applications in machine translation, bilingual lexicography, and crosslanguage information retrieval. This paper describes the automatic construction of a Japanese English lexicon from a Japanese English collection of summaries of technical conference papers using ....

....of the Japanese word J , then one would expect that when the Japanese word J is present in a Japanese sentence, its English translation E would also appear in the paired English sentence. A number of statical measures, such as mutual information, 2 like, likelihood ratio test based [7, 5], have been developed to compute the association signi cance between two events. We used the likelihood ratio test based measure developed by Dunning [5] to compute the association strength between a pair of Japanese English words. From the aligned sentences, we constructed a contingency table for ....

William A. Gale and Kenneth W. Church. Identifying word correspondences in parallel texts. In Proceedings of the Fourth DARPA Speech and Natual Language Workshop, pages 152-157, Pacic Grove, CA, 1991.

....their corpus of sentence pairs (a portion of the Hansard data) They do this by means of a particular version of the EM algorithm (Dempster al. 10] which should allow them to obtain complete coverage. However, the authors do not discuss the level of precision of their results. Gale Church [13] introduce a method for identifying some of the word correspondences in texts that have already been aligned at the sentence level. They first determine a set word pairs that are strongly associated in the sentence pairs. This is done by applying a c 2 like statistic to two by two contigency ....

Gale W., Church K., Identifying Word Correspondences in Parallel Texts, Proceedings of DARPA SLS Workshop, 1991.

....measured the distance in words rather than in characters. General bitext mapping algorithms are a recent invention. So far, most researchers interested in co occurrence of mutual translations have relied on bitexts where sentence boundaries (or other text unit boundaries) were easy to find (e.g. Gale Church, 1991; Kumano Hirakawa, 1994; Fung, 1995; Melamed, 1995) Aligned text segments suggest a boundary based model of cooccurrence, illustrated in Figure 2. For bitexts involving languages with similar word order, a more accurate combined model of co occurrence can be built using both segment boundary ....

W. Gale & K. W. Church. (1991) "Identifying Word Correspondences in Parallel Texts," Proceedings of the DARPA SNL Workshop. Asilomar, CA.

....specifying a priori probabilities or likelihood scores. Existing automatic methods for constructing N best translation lexicons rely on the availability of large training corpora of parallel texts in the source and target languages. For some methods, the corpora must also be aligned by sentence [Bro93, Gal91a]. Unfortunately, such training corpora are available for only a handful of language pairs, and the cost to create enough training data manually for new language pairs is very high. This paper presents 1. a new automatic evaluation method for N best translation lexicons, 2. a filter based approach ....

....pairs involving S or T in the same sentence pair. An alignment filter is based on the relative positions of S and T in their respective texts[Dag93] The decision procedure used to select lexicon entries from the multiset of candidate translation pairs is a variation of the method presented in [Gal91a]. Dun93] found binomial log likelihood ratios to be relatively accurate when dealing with rare tokens. This statistic was used to estimate dependencies between all co occuring (source word, target word) pairs. For each source word S, target words were ranked by their dependence with S. The top N ....

W. Gale & K. W. Church, "Identifying Word Correspondences in Parallel Texts," Proceedings of the DARPA SNL Workshop, 1991.

....not necessarily reliable anchor points. 2.3. Word and term translation Some of the algorithms used for alignment 2 produce a small bilingual lexicon as a by product [Kay Roscheisen1993, Chen1993] Some other algorithms use sentencealigned parallel texts to further compile a bilingual lexicon [Gale Church1991, Dagan et al..1993, Kupiec1993, Wu Xia1994, Dagan Church1994, Smadja McKeown1993] Note that all of the following algorithms, with the exception of 2 Some of the work cited actually finds the correct word ordering in the translation, while others do not. It was argued that word alignment ....

....2.4. Word alignment [Brown et al..1990, Brown et al..1993] are the first to use a stochastic sentence translation model. Estimation Maximization is used to estimate the parameters for the model. Their model produces word alignment from clean, sentence aligned parallel corpora during EM estimation. [Gale Church1991] propose using mutual information and t scores to find word correspondences as an alternative to the IBM model, noting that there is an explosive number of parameters to be estimated in the EM model when the vocabulary size is large. They use a progressive deepening method, starting with a small ....

Gale & Church1991. Gale, William & Kenneth Church. 1991. Identifying word correspondences in parallel text. In Proceedings of the Fourth Darpa Workshop on Speech and Natural Language, Asilomar.

....= 3 H LNC( 1 H RNC( 1 CNT( 3 H LNC( 0 H RNC( 1 Figure 11 The set of left and right neighboring characters of four strings and their corresponding entropies [Tung 1994] 2. 4 Automatic Comparison of Parallel Texts Parallel corpora, such as the Hansards corpus [Brown 1991a, Gale 1991a] are very useful knowledge sources for automatic acquisition of bi lingual (and monolingual) knowledge. In the field of computational linguistics, a variety of researches have investigated the use of bilingual corpora, including sentence alignment [Wu 1994] word correspondence [Dagan 1993] ....

....text is likely to correspond to another word in the target text that is highly associated with the source word. In addition, we must consider the relative position of the corresponding word in the other language, which also provides useful information due to the locality phenomena. For instance, Gale and Church (1991b) used the f 2 statistic, a c 2 like statistic [Hoel 1971] as a measure of the association of pairs of words to find the possible correspondence among words which had high word association. Interested readers are referred to [Gale 1991b, Su 1996] for definition of the f 2 statistic. ....

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Gale, William A. and Kenneth W. Church, "Identifying Word Correspondences in Parallel Texts," Proceedings of DARPA Speech and Natural Language Workshop, pp. 152-157, Pacific Grove, California, USA, 1991.

....integrates some word learning with a larger system for schema acquisition. Acquisition here is not limited to a single sentence of context, but an entire story. 7.2 Other Related Work 7.2. 1 Translation Lexicons This work also has ties to the work on automatic construction of translation lexicons (Gale Church, 1991; Catizone, Russell, Warwick, 1993; Kumano Hirakawa, 1994; Wu Xia, 1995; Melamed, 1995) These systems use input in the form of aligned pairs of sentences in two different natural languages. While most of these methods also compute association scores between pairs (in their case, word word ....

Gale, W., & Church, K. (1991). Identifying word correspondences in parallel texts. In Proceedings of the Fourth DARPA Speech and Natural Language Workshop.

....capital can have either one or two arguments. By using common substructures, we can combine these two stages in Wolfie. This work also has ties to the work on automatic construction of translation lexicons (Wu Xia 1995; Melamed 1995; Kumano Hirakawa 1994; Catizone, Russell, Warwick 1993; Gale Church 1991). While most of these methods also compute association scores between pairs (in their case, word word pairs) and use a greedy algorithm to choose the best translation(s) for each word, they do not take advantage of the constraints between pairs. One exception is Melamed (1996) however, his ....

Gale, W., and Church, K. 1991. Identifying word correspondences in parallel texts. In Proceedings of the Fourth DARPA Speech and Natural Language Workshop.

....for the extraction of translation lexicons from parallel corpora. The first step in deriving a translation lexicon is finding the correspondences between sentences. For the sentence alignment problem well documented solutions are available. We used an algorithm published by Gale and Church (W.A. Gale and K.W. Church 1993) that aligns sentences of a parallel corpus based on sentence lengths. Roughly spoken two approaches can be taken to find the translations of the words within the sentences: the hypothesis testing approach and the estimat 3 The Van Dale translation dictionary Dutch English (W. Martin and ....

W.A. Gale and K.W. Church (1991), Identifying word correspondences in parallel texts., In Fourth DARPA Workshop on Speech and Natural Language, pages152--157.

....for the entire text. 8 Brian Harris, 1992; Pierre Isabelle, 1992. 9 The general problem of text and phrase alignment reduces to the string correction problem. Wagner and Fischer, 1974; Lowrance and Wagner, 1975. Several discussions on text alignment have been published, for instance by W. Gale and Kenneth Church, 1991. 10 Hans Karlgren 1988. 11 Magnus Nordstr m and Paul Pettersson, 1993. TEXT PRODUCTION Context Display The first tool Dilemma provides the translator with is a look up tool that will enable browsing through the bitext and looking at text elements pairwise, located by a string search. The ....

Gale, W.A. and Kenneth Church. 1991. "Identifying Word Correspondences in Parallel Texts". In Proceedings of DARPA 91, pp 152-157.

....Others such as [6, 7] use an EM based model to align words in sentence pairs in order to obtain a technical lexicon. Some other algorithms use sentence aligned parallel texts to further compile a bilingual lexicon of technical words or terms using similarity measures on bilingual lexical pairs [21, 25, 29]. Yet others focus on translating phrases or terms which consist of multiple words [6, 25, 29] The main inspiration for our work [10, 14] to be described in the following section, comes from [21] who propose using word occurrences patterns and average mutual information and t scores to find word ....

.... lexicon of technical words or terms using similarity measures on bilingual lexical pairs [21, 25, 29] Yet others focus on translating phrases or terms which consist of multiple words [6, 25, 29] The main inspiration for our work [10, 14] to be described in the following section, comes from [21] who propose using word occurrences patterns and average mutual information and t scores to find word correspondences as an alternative to the IBM word alignment model. Given any pair of bilingual words, Lecture Notes in Computer Science 3 their occurrence patterns in all sentences are ....

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William Gale and Kenneth Church. Identifying word correspondences in parallel text. In Proceedings of the Fourth Darpa Workshop on Speech and Natural Language, Asilomar, 1991. Lecture Notes in Computer Science 17

....that capital can have either one or two arguments. By using common substructures, we can combine these two stages in Wolfie. This work also has ties to the work on automatic construction of translation lexicons (Wu and Xia, 1995; Melamed, 1995; Kumano and Hirakawa, 1994; Catizone et al. 1993; Gale and Church, 1991). While most of these methods also compute association scores between pairs (in their case, word word pairs) and use a greedy algorithm to choose the best translation(s) for each word, they do not take advantage of the constraints between pairs. One exception is Melamed (1996) however, his ....

W. Gale and K. Church. 1991. Identifying word correspondences in parallel texts. In Proceedings of the Fourth DARPA Speech and Natural Language Workshop.

....ontologies for the languages that we are interested in, to questions such as how much and what kind of information is really language specific. The ontologies will require access to information from many sources, not the least of which will be statistical information from aligned bilingual corpora (Gale and Church, 1991; Dagan et al. 1993; Wu, 1995; Wu and Xia, 1994) Unless we are claiming that no features need to be shared between language translation pairs, which we are not, a decision must still be made about what information should be transferred between the languages. A related question arises for ....

W. Gale and K. W. Church. Identifying word correspondences in parallel texts. In Proceedings of the ARPA SNL Workshop, 1991.

....correspondences, they are more general than alignments (Melamed, 1996a) For the same reason, bitext maps allow a more general definition of token cooccurrence. Early efforts at extracting translation lexicons from bitexts deemed two tokens to co occur if they occurred in aligned sentence pairs (Gale and Church, 1991). SABLE counts two tokens as cooccurring if their point of correspondence lies within a short distance ffi of the interpolated bitext map in the bitext space, as illustrated in Figure 1. To ensure that interpolation is well defined, minimal sets of non monotonic points of correspondence are ....

W. Gale and K. W. Church. 1991. "Identifying Word Correspondences in Parallel Texts". Proceedings of the DARPA SNL Workshop, 1991.

....corpus [Brown 91a, Gale 91a] are very useful knowledge sources for automatic acquisition of bilingual (and monolingual) knowledge. A variety of researches have investigated the use of bilingual corpora, including sentence alignment [Brown 91a, Gale 91a, Wu 94] word correspondence [Brown 91b, Gale 91b, Dagan 93] collocation correspondence [Smadja 92, Kupiec 93] word sense disambiguation [Brown 91b, Dagan 91, Gale 92] and machine translation [Brown 93, Su 95, Wu 95] In particular, the aligned bilingual corpora at the word level are valuable for bilingual lexicography [Smadja 96] and ....

....over all possible correspondences. The performance was evaluated on a sample of 800 sentences, where 61 of the English words were matched with some French words, and about 95 of these pairs were judged as being correctly matched. Readers who are interested in the details are referred to [Gale 91b] Besides single word correspondences, collocational correspondences have also been explored in [Smadja 92; Kupiec 93] In these approaches, mutual information is used as a measure of correspondence, and an iterative or EM based re estimation procedure is used to find the best correspondences. ....

Gale, W. A., and K. W. Church, "Identifying Word Correspondences in Parallel Texts," Proceedings of DARPA Speech and Natural Language Workshop, pp. 152-157, Pacific Grove, California, USA, 1991.

.... lexicon is sufficient and preferable for many multilingual NLP applications, including crummy MT on the World Wide Web (Church Hovy, 1993) certain machine assisted translation tools (e.g. Macklovitch, 1994; Melamed, 1996b) concordancing for bilingual lexicography (Catizone et al. 1993; Gale Church, 1991), computerassisted language learning, corpus linguistics (Melby, 1981) and cross lingual information retrieval (Oard Dorr, 1996) In this paper, we present a fast method for inducing accurate translation lexicons. The method assumes that words are translated one to one. This assumption reduces ....

....parameters can be conditioned on prior knowledge about the bitext to improve the model s accuracy. 2 Co occurrence With the exception of (Fung, 1995b) previous methods for automatically constructing statistical translation models begin by looking at word cooccurrence frequencies in bitexts (Gale Church, 1991; Kumano Hirakawa, 1994; Fung, 1995a; Melamed, 1995) A bitext comprises a pair of texts in two languages, where each text is a translation of the other. Word co occurrence can be defined in various ways. The most common way is to divide each half of the bitext into an equal number of segments ....

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W. Gale & K. W. Church, "Identifying Word Correspondences in Parallel Texts," Proceedings of the DARPA SNL Workshop, 1991.

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Gale, W. and K. Church "Identifying Word Correspondences in Parallel Texts," Proceedings of the DARPA Conference on Speech and Natural Language, 1991b.

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Gale, W. A., & Church, K. W. (1991a). "Identifying word correspondences in parallel texts". Fourth DARPA Workshop on Speech and Natural language, Asilomar, California.

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W. A. Gale and K. W. Church. Identifying word correspondences in parallel texts. In Proc. of the Speech and Natural Language Workshop, page 152, Pacific Grove, CA, 1991.

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W.A. Gale and K.W. Church. Identifying Word Correspondences in Parallel Texts. In Proceedings of the 4th Speech and Natural Language Workshop, pp. 152--157. DARPA, Morgan Kaufmann.

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W. Gale and K. Church. 1991. Identifying word correspondences in parallel texts. In Proceedings of the Forth Darpa Speech and Natural Language Processing Workshop, pp. 152--157 Pacific Grove, CA.

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W. Gale and K. Church (1991). "Identifying word correspondences in parallel texts," In Fourth DARPA Workshop on Speech and Natural Language, Morgan Kaufmann Publishers, pp. 152--157.

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Gale, W. A. and K. W. Church, (1991b). Identifying Word Correspondences in Parallel Texts, In Proceedings of the Fourth DARPA Speech and Natural Language Workshop, 152-157, Pacific Grove, CA, USA.

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W. Gale & K. W. Church, "Identifying Word Correspondences in Parallel Texts," DARPA SNL Workshop, 1991.

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Gale, W. A. and Church, K. W. (1991) \Identifying word correspondences in parallel texts." Proc. of DARPA Speech and Natural Language Workshop. p. 152-157.

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W. Gale & K. W. Church. (1991b) "Identifying Word Correspondences in Parallel Texts," Proceedings of the DARPA SNL Workshop. Asilomar, CA.

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W. Gale and K. Church (1991). "Identifying word correspondences in parallel texts," in Fourth DARPA Workshop on Speech and Natural Language, Morgan Kaufmann Publishers, pp. 152--157.

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Gale, W. A., & Church, K. W. (1991a). Identifying word correspondences in parallel texts. In Fourth DARPA Workshop on Speech and Natural Language, Asilomar, California.

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Gale, W.A. and K.W. Church. 1991. "Identifying Word Correspondences in Parallel Texts", in Proceedings of the 4th Speech and Natural Language Workshop, DARPA, Morgan Kaufmann.

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W. Gale & K. W. Church, "Identifying Word Correspondences in Parallel Texts," Proceedings of the DARPA SNL Workshop, 1991b.

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W. Gale & K. W. Church, "Identifying Word Correspondences in Parallel Texts," DARPA SNL Workshop, 1991.

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W.A. Gale and K.W. Church. "Identifying Word Correspondences in Parallel Texts". In Proceedings of the 4th Speech and Natural Language Workshop, pp. 152--157. DARPA, Morgan Kaufmann, 1991.

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W. Gale & K. W. Church, "Identifying Word Correspondences in Parallel Texts," Proceedings of the DARPA SNL Workshop, 1991.

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Gale, W.A., and K.W. Church. 1991b. Identifying word correspondences in parallel texts. Proceedings of the Fourth DARPA Speech and Natural Language Workshop, 152-157.

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Gale, William A. and Church, Ken W. 1991b Identifying Word Correspondences in Parallel Texts To appear.

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W. Gale & K. W. Church, "Identifying Word Correspondences in Parallel Texts," DARPA SNL Workshop, 1991.

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