Learning and inference for clause identification

Xavier Carreras, Lluís Màrquez, Vasin Punyakanok, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper presents an approach to partial parsing of natural language sentences that makes global inference on top of the outcome of hierarchically learned local classifiers. The best decomposition of a sentence into clauses is chosen using a dynamic programming based scheme that takes into account previously identified partial solutions. This inference scheme applies learning at several levels-when identifying potential clauses and when scoring partial solutions. The classifiers are trained in a hierarchical fashion, building on previous classifications. The method presented significantly outperforms the best methods known so far for clause identification.

Original languageEnglish (US)
Title of host publicationMachine Learning
Subtitle of host publicationECML 2002 - 13th European Conference on Machine Learning, Proceedings
EditorsTapio Elomaa, Heikki Mannila, Hannu Toivonen
Number of pages13
ISBN (Print)9783540440369
StatePublished - 2002
Externally publishedYes
Event13th European Conference on Machine Learning, ECML 2002 - Helsinki, Finland
Duration: Aug 19 2002Aug 23 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other13th European Conference on Machine Learning, ECML 2002

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Learning and inference for clause identification'. Together they form a unique fingerprint.

Cite this