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Head-Driven Statistical Models for Natural Language Parsing

by Michael Collins , 1999
"... ..."
Abstract - Cited by 1145 (16 self) - Add to MetaCart
Abstract not found

Accurate Unlexicalized Parsing

by Dan Klein, Christopher D. Manning - IN PROCEEDINGS OF THE 41ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS , 2003
"... We demonstrate that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar. Indeed, its ..."
Abstract - Cited by 1026 (70 self) - Add to MetaCart
We demonstrate that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar. Indeed, its

Shallow Parsing with Conditional Random Fields

by Fei Sha, Fernando Pereira , 2003
"... Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluati ..."
Abstract - Cited by 575 (8 self) - Add to MetaCart
optimization algorithms were critical in achieving these results. We present extensive comparisons between models and training methods that confirm and strengthen previous results on shallow parsing and training methods for maximum-entropy models.

Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network

by Kristina Toutanova , Dan Klein, Christopher D. Manning, Yoram Singer - IN PROCEEDINGS OF HLT-NAACL , 2003
"... We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective ..."
Abstract - Cited by 660 (23 self) - Add to MetaCart
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii

Three Generative, Lexicalised Models for Statistical Parsing

by Michael Collins , 1997
"... In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free gram- mar. We then extend the model to in- clude a probabilistic treatment of both subcategorisation and wh~movement. Results on Wall Street Journal text show that the parse ..."
Abstract - Cited by 567 (8 self) - Add to MetaCart
In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free gram- mar. We then extend the model to in- clude a probabilistic treatment of both subcategorisation and wh~movement. Results on Wall Street Journal text show

A Maximum Entropy Model for Part-Of-Speech Tagging

by Adwait Ratnaparkhi , 1996
"... This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "features" t ..."
Abstract - Cited by 577 (1 self) - Add to MetaCart
;features" to predict the POS tag. Furthermore, this paper demonstrates the use of specialized features to model difficult tagging decisions, discusses the corpus consistency problems discovered during the implementation of these features, and proposes a training strategy that mitigates these problems.

WordNet: An on-line lexical database

by George A. Miller, Richard Beckwith, Christiane Fellbaum, Derek Gross, Katherine Miller - International Journal of Lexicography , 1990
"... WordNet is an on-line lexical reference system whose design is inspired by current ..."
Abstract - Cited by 1945 (9 self) - Add to MetaCart
WordNet is an on-line lexical reference system whose design is inspired by current

Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging

by Eric Brill - Computational Linguistics , 1995
"... this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learni ..."
Abstract - Cited by 916 (7 self) - Add to MetaCart
of this learning method applied to part of speech tagging

Text Chunking using Transformation-Based Learning

by Lance A. Ramshaw, Mitchell P. Marcus , 1995
"... Eric Brill introduced transformation-based learning and showed that it can do part-ofspeech tagging with fairly high accuracy. The same method can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive "baseNP" chunks. For ..."
Abstract - Cited by 509 (0 self) - Add to MetaCart
. For this purpose, it is convenient to view chunking as a tagging problem by encoding the chunk structure in new tags attached to each word. In automatic tests using Treebank-derived data, this technique achieved recall and precision rates of roughly 92% for baseNP chunks and 88% for somewhat more complex chunks

TnT - A Statistical Part-Of-Speech Tagger

by Thorsten Brants , 2000
"... Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison h ..."
Abstract - Cited by 525 (5 self) - Add to MetaCart
Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison
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