TAILIEUCHUNG - Báo cáo khoa học: "Towards History-based Grammars: Using Richer Models for Probabilistic Parsing*"

We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of. | Towards History-based Grammars Using Richer Models for Probabilistic Parsing Ezra Black Fred Jelinek John Lafferty David M. Magerman Robert Mercer Salim Roukos IBM T. J. Watson Research Center Abstract We describe a generative probabilistic model of natural language which we call HBG that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical syntactic semantic and structural information from the parse tree into the disambiguation process in a novel way. We use a corpus of bracketed sentences called a Treebank in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of generating the correct parse. In head-to-head tests against one of the best existing robust probabilistic parsing models which we call P-CFG the HBG model significantly outperforms P-CFG increasing the parsing accuracy rate from 60 to 75 a 37 reduction in error. Introduction Almost any natural language sentence is ambiguous in structure reference or nuance of meaning. Humans overcome these apparent ambiguities by examining the context of the sentence. But what exactly is context Frequently the correct interpretation is apparent from the words or constituents immediately surrounding the phrase in question. This observation begs the following question How much information about the context of a sentence or phrase is necessary and sufficient to determine its meaning This question is at the crux of the debate among computational linguists about the application and implementation of statistical methods in natural language understanding. Previous work on disambiguation and probabilistic parsing has offered partial answers to this question. Hidden Markov models of words and Thanks to Philip Resnik and Stanley Chen for their valued input. their tags .

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• Towards History based Grammars
• Richer Models
• Probabilistic Parsing
• báo cáo khoa học
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