Collins, M. (1997). Three Generative, Lexicalised Models for Statistical Parsing. In Proceedings of ACL-1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....In an effort to improve question focus recognition this year, we trained the Brill part ofspeech tagger [2] on questions from TREC 8, TREC 9 and HowStuffWorks. 7] The resulting rules were used to tag the TREC 10 questions. The tagged questions were then run through the Collins parser [3] [4] for a full parse. There are three steps to question focus assignment. In the first step, the question type is determined using predefined search patterns based on regular expressions. There are 7 special question types (acronym, counterpart, definition, famous, standfor, synonym, why) and 7 ....

Collins, Michael. (1997). Three Generative, Lexicalised Models for Statistical Parsing. Proceedings of the 35 Annual Meeting of the ACL (jointly with the 8th Conference of the EACL), Madrid. Available at <http://www.research.att.com/~mcollins/papers/paper14.short.ps>.

....intensive research. An interesting feature common to most such models is the incorporation of contextual dependencies on individual head words into rule based probability models. Such word based lexicalizations of probability models are used successfully in the statistical parsing models of, e.g. Collins (1997), Charniak (1997) or Ratnaparkhi (1997) However, it is still an open question which kind of lexicalization, e.g. statistics on individual words or statistics based upon word classes, is the best choice. Secondly, these approaches have in common the fact that the probability models are trained ....

Michael Collins. 1997. Three generative, lexicalised models for statistical parsing. In Proceedings of the 35th ACL, Madrid.

....1993] In this kind of work, morphological analysis has been of minor concern: actual word occurrences and their POS tags, instead of morphemes, are utilized in probabilistic language models. Impoverished forms of morphological analysis are often consulted in order to deal with unknown words (see [Collins, 1997, Charniak, 1999] Clearly, statistics over word occurrence is more prone to sparse data problems than morpheme occurrence. However, for English, the existence of large syntactic tree banks, extended POS 5 tag sets and the less complex nature of English word structure, have been instrumental in ....

....an important source for statistics over linguistic phenomena, which can be employed for resolving ambiguities during processing. In probabilistic syntactic parsing in particular, a tree bank is used for inducing probabilistic grammars e.g. Scha, 1990, Bod, 1992, Magerman, 1995, Bod, 1995, Collins, 1997, Charniak, 1999, Sima an, 2000] A probabilistic language model consists of a probabilistic grammar and a model of how probabilities of parse trees and sentences are derived. In a probabilistic grammar, a formal grammar is extended with a nite set of conditional probabilities, each associated ....

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Collins, M. (1997). Three generative, lexicalized models for statistical parsing. In Proceedings of the 35th Annual Meeting of the ACL and the 8th Conference of the EACL, pages 16-23, Madrid, Spain.

....the Tree gram model, beyond those underlying DOP, is that a tree bank of analysed utterances, i.e. parse trees, can be viewed di erently if we abandon the atomicity of linguistically motivated CFG rules. This has been exploited in the statistical parsing literature [Eisner, 1996, Charniak, 1997a, Collins, 1997, Charniak, 1999] for parsing with head driven bilexical dependencies in the so called Markov Grammars 1 . In the Treegram model, we generalize over Markov Grammars as well as the DOP model by extracting constructs from the tree bank trees that abide neither by the atomicity of CFG rules nor by ....

....and with surrounding syntactic information. Indeed, as Charniak [Charniak, 1997b] observes, lexicalization with actual words is the biggest change in statistical parsing over the last few years. Head lexicalization currently pervades in the parsing literature e.g. Eisner, 1996, Collins, 1996, Collins, 1997, Charniak, 1997a, Charniak, 1999, Ratnaparkhi, 1997] This method extends every treebank nonterminal with its head word: the model is trained on this head lexicalized treebank. Head lexicalized models extract probabilistic relations between pairs of lexicalized nonterminals ( bilexical ....

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Collins, M. (1997). Three generative, lexicalized models for statistical parsing. In Proceedings of the 35th Annual Meeting of the ACL and the 8th Conference of the EACL, pages 16-23, Madrid, Spain.

.... often perform as well as other broad coverage parsing systems for predicting tree structure from POS tags (Charniak, 1996) In addition, many more sophisticated parsing models are elaborations of such PCFG models, so understanding the properties of PCFGs is likely to be useful (Charniak, 1997; Collins, 1997). It is well known that natural language exhibits dependencies that Context Free Grammars (CFGs) and hence PCFGs, cannot describe (Shieber, 1985) But as explained below, the independence assumptions implicit in PCFGs introduce biases in the statistical model induced from a tree bank even in ....

Collins, Michael. 1997. Three generative, lexicalised models for statistical parsing. In The Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, San Francisco. Morgan Kaufmann.

....shallow syntactic information can be extracted using local information by examining the pattern itself, its nearby context and the local part of speech information. Thus, over the past few years, along with advances in the use of learning and statistical methods for acquisition of full parsers (Collins, 1997; Charniak, 1997a; Charniak, 1997b; Ratnaparkhi, 1997) signi cant progress has been made on the use of statistical learning methods to recognize shallow parsing patterns syntactic phrases or words that participate in a syntactic relationship (Church, 1988; Ramshaw and Marcus, 1995; Argamon et ....

....was the desire to get better performance and higher reliability. However, since work in this direction has started, a signi cant progress has also been made in the research on statistical learning of full parsers, both in terms of accuracy and processing time (Charniak, 1997b; Charniak, 1997a; Collins, 1997; Ratnaparkhi, 1997) This paper investigates the question of whether work on shallow parsing is worthwhile. That is, we attempt to evaluate quantitatively the intuitions described above that learning to perform shallow parsing could be more accurate and more robust than learning to generate ....

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M. Collins. 1997. Three generative, lexicalised models for statistical parsing. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics.

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Collins, M. (1997). Three Generative, Lexicalised Models for Statistical Parsing. In Proceedings of ACL-1997.

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M. J. Collins. 1997. Three generative, lexicalised models for statistical parsing. In Proceedings of the 35th Annual Meeting of the ACL, Madrid. Association for Computational Linguistics.

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Collins, M.J. 1997. Three generative, lexicalised models for statistical parsing. In The Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, pages 16--23.

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Collins, M. 1997. Three generative, lexicalized models for statistical parsing. Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (pp. 16-23). Madrid, Spain.

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Collins, M. 1997. Three generative, lexicalised models for statistical parsing. In Proc. Assoc. for Comp. Linguistics, 16--23.

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Michael Collins. 1997. Three generative, lexicalized models for statistical parsing. In proceedings of the ACL97, Madrid, Spain.

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M. Collins. 1997. Three generative, lexicalised models for statistical parsing. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistic, Madrid, Spain.

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Collins, Michael. 1997. Three generative, lexicalised models for statistical parsing. In Proceedings of the 35th Annual Conference of the Association for Computational Linguistics (ACL-97), pages 16--23, Madrid, Spain.

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Michael Collins. 1997. Three Generative, Lexicalized Models for Statistical Parsing. In 35th Annual Meeting of the ACL.

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Collins, Michael. (1997). Three Generative, Lexicalised Models for Statistical Parsing. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, pages 16-23.

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Collins, M. 1997. Three generative, lexicalized models for statistical parsing. Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (pp. 16-23). Madrid, Spain.

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Michael Collins. 1997. Three generative, lexicalised models for statistical parsing. In Proc. of ACL-97.

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Michael Collins. 1997. Three generative, lexicalized models for statistical parsing. In Proceedings of the ACL'97,Somerset, New Jersey.

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M. Collins. 1997. Three generative, lexicalised models for statistical parsing Proceedings of 35th Annual Meeting of the ACL, Madrid, Spain.

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Collins, M. (1997). Three generative, lexicalized models for statistical parsing. In Proceedings of the 35th Annual Meeting of the ACL (jointly with the 8th Conference of the EACL), Madrid.

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M. J. Collins. 1997. Three generative, lexicalised models for statistical parsing. In Proceedings of the 35th Annual Meeting of the ACL, Madrid. Association for Computational Linguistics.

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Michael John Collins. 1997. Three generative, lexicalised models for statistical parsing. In ACL 35/EACL 8, pages 16--23.

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Michael Collins. 1997. Three generative, lexicalised models for statistical parsing. In Proceedings of the 35th Annual Meeting of the ACL.

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Michael John Collins. 1997. Three generative, lexicalised models for statistical parsing. In ACL 35/EACL 8, pages 16--23.

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