Bruce, Rebecca and Janyce Wiebe (1994). "Word-Sense Disambiguation Using Decomposable Models", in Proceedings of ACL94.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....features that are irrelevant to the disambiguation decision. See (Golding and Roth, 1996) For word sense disambiguation methods, see also (Black, 1988; Brown et al. 1991; Guthrie et al. 1991; Gale, Church, and Yarowsky, 1992; McRoy, 1992; Leacock, Towell, and Voorhees, 1993; Yarowsky, 1993; Bruce and Wiebe, 1994; Niwa and Nitta, 1994; Voorhees, Leacock, and Towell, 1995; Yarowsky, 1995; Golding and Schabes, 1996; Ng and Lee, 1996; Fujii et al. 1996; Schutze, 1997; Schutze, 1998) A probabilistic decision list (Yamanishi, 1992a) is a kind of conditional distribution and di#erent from a deterministic ....

....case slots in a class based model are mutually independent. The method of using a dependency forest model is not limited to just the problem of learning dependencies between case slots. It is potentially useful in other natural language processing tasks, such as word sense disambiguation (cf. (Bruce and Wiebe, 1994)) X 1 (5) 6) 7) 2) 3) 4) P (X 1 )P (X 2 )P (X 3 ) 1) Figure 5.1: Example dependency forests. Algorithm: 1. Let T : #; 2. Let V = ....

Bruce, Rebecca and Janyce Wiebe. 1994. Word-sense disambiguation using decomposable models. Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, pages 139--145.

....the graph G # = V #,E E # ) with E # = #,v)#v # V d ,is chordal. 7 Conclusions Decomposable models possess several important characteristics that make them an appealing class of statistical models, as has been observed in applied contexts ranging from word sense disambiguation [BW94] to multi dimensional histograms [DGR01] E#cient algorithms, however, are essential if this class of models is to be exploited in large scale problems. In this paper, we have presented an e#cient new algorithm for performing stepwise selection in decomposable models. The enumeration of edges ....

R. Bruce and J. Wiebe. Word-sense disambiguation using decomposable models. In ACL, Las Cruces, NM, 1994.

....may provide insights into the relationships that exist among features and more general rules of disambiguation. 9 Related Work Bigrams have been used as features for word sense disambiguation, particularly in the form of collocations where the ambiguous word is one component of the bigram (e.g. (Bruce and Wiebe, 1994), Ng and Lee, 1996) Yarowsky, 1995) While some of the bigrams we identify are collocations that include the word being disambiguated, there is no requirement that this be the case. Decision trees have been used in supervised learning approaches to word sense disambiguation, and have fared ....

R. Bruce and J. Wiebe. 1994. Word-sense disambiguation using decomposable models. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, pages 139{ 146.

.... with decision trees or traditional non interpolated decision lists (Rivest, 1987) They also tend to be effective at modelling a large number of highly non independent features that can be problematic to model fully in Bayesian topologies for sense disambiguation (Gale, Church and Yarowsky, 1992; Bruce and Wiebe, 1994). This paper presents a new learning topology for sense disambiguation based on hierarchical decision lists, adding a useful degree of conditional branching to the decision list framework. The paper also includes a comprehensive evaluation of this algorithm s performance on extensive previously ....

Bruce, R. and J. Wiebe: 1994, Word-sense disambiguation using decomposable models. In Proceedings of ACL '94, pp. 139-146, Las Cruces, NM.

....of the Cambridge International Dictionary of English (CIDE, 1995) by the lexicographers working on the dictionary. The sample to be disambiguated was selected on a sentence by sentence, rather than wordtype by word type basis. The data is available for research. 3.2. 4 Bruce, Wiebe et al. Bruce and Wiebe (1994), Wiebe et al. 1997) and (Bruce and Wiebe, 1998) report on a series of exercises in manual tagging, explicitly within the context of WSD training and testing. In the first exercise, 2369 sentences containing the noun interest (or its plural form, interests) were tagged. More recently, a total of ....

Bruce, Rebecca and Janyce Wiebe. 1994. Word sense disambiguation using decomposable models. In Proc. 32nd Annual Meeting of the ACL, pages 139--145, Las Cruces, New Mexico. ACL.

.... of NLP research using MRDs include amongst others sublanguage analysis (Walker and Amsler, 1987) knowledge acquisition and organisation (Alshawi, 1987; Calzolari and Picchi, 1988; Wilks et al., 1989; Kwong, 1998) word sense disambiguation (Lesk, 1986; Veronis and Ide, 1990; Guthrie et al., 1991; Bruce and Wiebe, 1994; Rigau et al., 1997) information retrieval (Krovetz and Croft, 1992) information extraction (Cowie et al., 1993) and text coherence (Kozima and Furugori, 1993) On line dictionaries seemed to offer the possibility for enormous savings in time and human and the problem changed from one of how to ....

Bruce, R. and Wiebe, J. 1994. Word-sense Disambiguation Using Decomposable Models. In Proceedings of the 32 nd Annual Meeting of the Association for Computational Linguistics (ACL-94).

....using only tag level models without lexical sensitivities besides the priors. ffl Standard annotated corpora of adequate size have long been available. In contrast, approaches to WSD attempt to take advantage of many different sources of information (e.g. see McRoy 1992; Ng and Lee 1996; Bruce and Wiebe 1994; Wilks and Stevenson 1998) it seems possible to obtain benefit from sources ranging from local collocational clues (Yarowsky 1993) to membership in semantically or topically related word classes (Yarowsky 1992; Resnik 1993) to consistency of word usages within a discourse (Gale et al. 1992a) ....

R. Bruce and J. Wiebe. (1994). Word-sense disambiguation using decomposable models. In Proceedings of ACL '96, Las Cruces, NM., pp. 139-146.

....ourselves to recent methods for word sense disambiguation that seem to be promising candidates for the detection of inadvertent semantic errors. The references are Schutze (1992, 1998) Yarowsky (1992, 1995) Liddy and Paik (1993) Weischedel, Meteer, Schwartz, Ramshaw, and Palmucci (1993) Bruce and Wiebe (1994, 1999) Lin (1997) Pedersen, Bruce, and Wiebe (1997) Karov and Edelman (1998) Leacock, Chodorow, and Miller (1998) Pedersen and Bruce (1998) Towell and Voorhees (1998) Pedersen (1999) and Whaley (1999) For a reasonable evaluation of the cited methods, we suggest that a given scheme not ....

....the above rules when one takes xm of the above rules to be v i , v j , w j , and w i , respectively, and selects the applicable texts. The characteristic vectors needed in Algorithm REGULAR WORD TEST and Algorithm SPECIAL WORD TEST are constructed analogously. Some previous work (for example, see Bruce and Wiebe (1994, 1999) Pedersen, Bruce, and Wiebe (1997) Pedersen and Bruce (1998) Pedersen (1999) uses similar encodings where words, parts of speech, and morphological features near a given word instance are recorded. Here, our list of parts of speech has 46 items that accommodate all morphological ....

Bruce, R., and Wiebe, J., Word-sense disambiguation using decomposable models, Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics (ACL-94), 1994, 139--146.

....cial Intelligence (www.aaai.org) All rights reserved. the test phase. Although high accuracy can be achieved with these approaches, a huge amount of work is necessary to manually tag words to be disambiguated. For the disambiguation of the noun interest with an accuracy of 78 , as reported in (Bruce and Wiebe, 1994), 2,476 usages of interest were manually assigned with sense tags from the Longman Dictionary of Contemporary English (LDOCE) For the LEXAS system, described in (Ng and Lee 1996) the high accuracy is due in part to the use of a large corpora. For this system, 192,800 word occurrences have been ....

Bruce, R. and Wiebe, J. 1994 Word Sense Disambiguation using Decomposable Models, Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics (ACL-94), LasCruces, 1994.

....1992) Ng and Lee, 1996) 3. WSD that use information gathered from raw corpora (unsupervised training methods) Yarowsky, 1995) Resnik, 1997) There are also hybrid methods that combine several sources of knowledge such as lexicon information, heuristics, collocations and others (McRoy, 1992) (Bruce and Wiebe, 1994) (Ng and Lee, 1996) Rigau et al. 1997) Statistical methods produce high accuracy results for small number of preselected words. A lack of widely available semantically tagged corpora almost excludes supervised learning methods. A possible solution for automatic acquisition of sense tagged ....

R. Bruce and J. Wiebe. 1994. Word sense disambiguation using decomposable models.

....use information gathered from raw corpora (unsupervised training methods) Yarowski [45] and Resnik [37] presented unsupervised WSD methods. There are also hybrid methods that combine several sources of knowledge such as lexicon information, heuristics, collocations and others: McRoy [26] Bruce [7], Ng [35] and Rigau [39] Statistical methods produce high accuracy results for small number of preselected words. A lack of widely available semantically tagged corpora almost excludes supervised learning methods. A possible solution for automatic acquisition of sense tagged corpora has been ....

Bruce, R., and Wiebe, J. Word Sense Disambiguation using decomposable models. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics (ACL-94) (LasCruces, NM, June 1994), pp. 139-146.

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Bruce, Rebecca and Wiebe, Janyce (1994b). WordSense Disambiguation Using Decomposable Models. Proceedings of the 32rd Annual Meeting of the ACL, Las Cruces, NM.

....method, identified a model incorporating these features for use in disambiguating the noun interest. These features, which are assigned automatically, are of three types: morphological, collocation specific, and class based, with part of speech (POS) categories serving as the word classes (see [3] for how the features were chosen) The results of using the model to disambiguate the noun interest were encouraging. We suspect that the model provides a description of the distribution of sense tags and contextual features that is applicable to a wide range of content words. This paper ....

....section, a probabilistic model was developed for disambiguating the noun senses of interest utilizing automatically identifiable contextual features that were considered to be intuitively applicable to all content words. The complete process of feature selection and model selection is described in [3]. Here, we describe the extension of that model to other content words. In essence, what we are describing is not a single model, but a model schema. The values of the variables included in the model change with the word being disambiguated as stated below. The model schema incorporates three ....

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Bruce, Rebecca and Wiebe, Janyce. Word-Sense Disambiguation Using Decomposable Models. Unpublished manuscript.

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Bruce, Rebecca and Janyce Wiebe (1994). "Word-Sense Disambiguation Using Decomposable Models", in Proceedings of ACL94.

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Bruce, R. and Wiebe, J. (1994). Word-sense disambiguation using decomposable models. In Proceedings of the 32nd Annual Meeting of the Association. for Computational Linguistics (ACL), Las Cruces, pp.139-145.

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R. Bruce and W. Janyce. Word sense disambiguation using decomposable models. In Proceedings of 33rd Annual Meeting of the Association for Computational Linguistics, 1994.

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R. Bruce and J. Wiebe, "Word-sense disambiguation using decomposable models", Proceedings of the 32 nd annual meeting of the Association for Computational Linguistics (ACL-94), 1994.

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, pages 139-146, LasCruces, NM, June 1994.

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R. Bruce and J. Wiebe, "Word-sense disambiguation using decomposable models", Proceedings of the 32 nd annual meeting of the Association for Computational Linguistics (ACL-94), 1994.

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R. Bruce and J. Wiebe, 1994. Word sense disambiguation using decomposable models. In Proceedings of the 32 na Annual Meeting of the Association for Computational Lingusitics, pages 139-145, Las Cruces, New Mexico.

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Bruce, R. and Wiebe, J. Word sense disambiguation using decomposable models. Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics (ACL-94), pages 139-146, LasCruces, New Mexico, 1994.

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Rebecca Bruce and Janyce Wiebe. 1994. Word Sense Disambiguation using Decomposable Models. In Proceedings of ACL-94.

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Rebecca Bruce and Janyce Wiebe. 1994. Wordsense disambiguation using decomposable models. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, New Mexico.

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Bruce, R. and Wiebe, J. (1994) Word-sense disambiguation using decomposable models, Proc. ACL-94.

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Bruce, R. and Wiebe, J. 1994b. "Word-sense disambiguation using decomposable models," Proc. 32nd Annual Meeting of the Assoc. for Computational Linguistics (ACL--94): 139-146.

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