A maximum entropy approach to natural language processing
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Statistical methods for speech recognition
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Stochastic attribute-value grammars
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Supertagging: an approach to almost parsing
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ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Probabilistic disambiguation models for wide-coverage HPSG parsing
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The importance of supertagging for wide-coverage CCG parsing
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Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Maximum entropy estimation for feature forests
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Extremely lexicalized models for accurate and fast HPSG parsing
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A statistical constraint dependency grammar (CDG) parser
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Efficient HPSG parsing with supertagging and CFG-filtering
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Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
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LAW V '11 Proceedings of the 5th Linguistic Annotation Workshop
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Future Generation Computer Systems
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This paper describes a log-linear model with an n-gram reference distribution for accurate probabilistic HPSG parsing. In the model, the n-gram reference distribution is simply defined as the product of the probabilities of selecting lexical entries, which are provided by the discriminative method with machine learning features of word and POS n-gram as defined in the CCG/HPSG/CDG supertagging. Recently, supertagging becomes well known to drastically improve the parsing accuracy and speed, but supertagging techniques were heuristically introduced, and hence the probabilistic models for parse trees were not well defined. We introduce the supertagging probabilities as a reference distribution for the log-linear model of the probabilistic HPSG. This is the first model which properly incorporates the supertagging probabilities into parse tree's probabilistic model.