Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/130453
Title: Using a Dependency Structure Parser without any Grammar Formalism to Analyse a Software Manual Corpus
Authors: Ting, C.H.A. 
Shiuan, P.L. 
Issue Date: 1996
Source: Ting, C.H.A., Shiuan, P.L. (1996). Using a Dependency Structure Parser without any Grammar Formalism to Analyse a Software Manual Corpus. Using a Dependency Structure Parser without any Grammar Formalism to Analyse a Software Manual Corpus. ScholarBank@NUS Repository.
Abstract: DESPAR is discussed, a hybrid approach to parsing that is based on an enhanced hidden Markov model & relies on no grammar formalism. The approach is corpus-based & statistical. Implementation builds on the insight of M. Liberman (1993) that dependency parsing is a kind of tagging for parts of speech. DESPAR takes tagged sentences as input, seeks candidate governors for each part of speech, eliminates invalid candidates for governor, & returns a likely dependency structure as output. The enhanced hidden Markov model operates with bigrams & uses a dynamic context algorithm & dependency axioms. The statistical part-of-speech tagger is based on the Brown & Wall Street Journal corpora, totaling almost 180,000 sentences. A module to handle unknown words effectively gives the parser unlimited vocabulary. A divide-and-conquer module simplifies complex sentences before parsing. DESPAR was applied to the software manual corpus in two stages; with original grammar & vocabulary & with added vocabulary. Preprocessing consisted of tokenization. it is concluded that no grammar formalism is required to analyze the dependency structure of a sentence. The performance of the parser could be improved by providing more corpora & by refining the enhanced hidden Markov model to use trigram transitions. L. Lagerquist.
Source Title: Using a Dependency Structure Parser without any Grammar Formalism to Analyse a Software Manual Corpus
URI: http://scholarbank.nus.edu.sg/handle/10635/130453
ISBN: 904200102X
Appears in Collections:Staff Publications

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