• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations

DMCA

Three Generative, Lexicalised Models for Statistical Parsing (1997)

Cached

  • Download as a PDF

Download Links

  • [acl.ldc.upenn.edu]
  • [www.aclweb.org]
  • [ucrel.lancs.ac.uk]
  • [aclweb.org]
  • [wing.comp.nus.edu.sg]
  • [aclweb.org]
  • [www.dei.unipd.it]
  • [wing.comp.nus.edu.sg]
  • [www.aclweb.org]
  • [arxiv.org]
  • [arxiv.org]
  • [user.phil-fak.uni-duesseldorf.de]
  • [www.clsp.jhu.edu]

  • Other Repositories/Bibliography

  • DBLP
  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by Michael Collins
Citations:570 - 8 self
  • Summary
  • Citations
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@MISC{Collins97threegenerative,,
    author = {Michael Collins},
    title = {Three Generative, Lexicalised Models for Statistical Parsing},
    year = {1997}
}

Share

Facebook Twitter Reddit Bibsonomy

OpenURL

 

Abstract

In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free gram- mar. We then extend the model to in- clude a probabilistic treatment of both subcategorisation and wh~movement. Results on Wall Street Journal text show that the parser performs at 88.1/87.5% constituent precision/recall, an average improvement of 2.3% over (Collins 96).

Keyphrases

statistical parsing    probabilistic treatment    wh movement    new statistical parsing model    lexicalised context-free gram mar    generative model    average improvement    constituent precision recall    wall street journal text show    parser performs   

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University