Treebank-Based Probabilistic Phrase Structure Parsing

Bibliographic Data

ID4796272
AuthorsAoife Cahill (University of Stuttgart, corresponding author)
Year2008
Volume2
Issue1
Pages36-58
Publication date2008-01-01
Open AccessYes
TypeARTICLE
VenueLanguage and Linguistics Compass (JOURNAL)
Journal identifiersISSN: 1749-818X • E-ISSN: 1749-818X
PublisherWiley (PUBLISHER • GB)
DOI10.1111/j.1749-818x.2007.00046.x
OpenAlexW2045918841
LanguageEN
Citations received1
References cited26

The area of probabilistic phrase structure parsing has been a central and active field in computational linguistics. Stochastic methods in natural language processing, in general, have become very popular as more and more resources become available. One of the main advantages of probabilistic parsing is in disambiguation: it is useful for a parsing system to return a ranked list of potential syntactic analyses for a string. In this article, we introduce probabilistic context-free grammars (PCFGs) and outline some of their strengths and weaknesses. We concentrate on the automatic extraction of stochastic grammars from treebanks (large collections of hand-corrected syntactic structures). We describe the current state of the field and the current research on improving the basic PCFG model. This includes lexicalized, history-based and generative models. Finally, we briefly mention some research into probabilistic phrase structure parsing for domains other than traditional treebank text and languages other than English (Chinese, Arabic, German and French

Computational linguistics · Natural language processing · Parsing · Phrase · Probabilistic logic · Rule-based machine translation · Treebank · Computer Science · Mathematics · Natural Language Processing Techniques · Speech and dialogue systems · Topic Modeling · Artificial Intelligence

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