N-gram Parsing for Jointly Training a Discriminative Constituency Parser
نویسندگان
چکیده
منابع مشابه
N-gram Parsing for Jointly Training a Discriminative Constituency Parser
Syntactic parsers are designed to detect the complete syntactic structure of grammatically correct sentences. In this paper, we introduce the concept of n -gram parsing, which corresponds to generating the constituency parse tree of n consecutive words in a sentence. We create a stand-alone n -gram parser derived from a baseline full discriminative constituency parser and analyze the characteri...
متن کاملN - gram Parsing for Jointly Training a Discriminative Constituency
Syntactic parsers are designed to detect the complete syntactic structure of grammatically correct sentences. In this paper, we introduce the concept of n-gram parsing, which corresponds to generating the constituency parse tree of n consecutive words in a sentence. We create a stand-alone n-gram parser derived from a baseline full discriminative constituency parser and analyze the characterist...
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In this study, we tackle the problem of self-training a feature-rich discriminative constituency parser. We approach the self-training problem with the assumption that while the full sentence parse tree produced by a parser may contain errors, some portions of it are more likely to be correct. We hypothesize that instead of feeding the parser the guessed full sentence parse trees of its own, we...
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We present a method for conditional maximum likelihood estimation of N-gram models used for text or speech utterance classification. The method employs a well known technique relying on a generalization of the Baum-Eagon inequality from polynomials to rational functions. The best performance is achieved for the 1-gram classifier where conditional maximum likelihood training reduces the class er...
متن کاملIntegrating Joint n-gram Features into a Discriminative Training Framework
Phonetic string transduction problems, such as letter-to-phoneme conversion and name transliteration, have recently received much attention in the NLP community. In the past few years, two methods have come to dominate as solutions to supervised string transduction: generative joint n-gram models, and discriminative sequence models. Both approaches benefit from their ability to consider large, ...
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ژورنال
عنوان ژورنال: Polibits
سال: 2013
ISSN: 2395-8618,1870-9044
DOI: 10.17562/pb-47-1