Causal Relation extraction

Eduardo Blanco, Nuria Castell, Dan Moldovan

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

75 Scopus citations

Abstract

This paper presents a supervised method for the detection and extraction of Causal Relations from open domain text. First we give a brief outline of the definition of causation and how it relates to other Semantic Relations, as well as a characterization of their encoding. In this work, we only consider marked and explicit causations. Our approach first identifies the syntactic patterns that may encode a causation, then we use Machine Learning techniques to decide whether or not a pattern instance encodes a causation. We focus on the most productive pattern, a verb phrase followed by a relator and a clause, and its reverse version, a relator followed by a clause and a verb phrase. As relators we consider the words as, after, because and since. We present a set of lexical, syntactic and semantic features for the classification task, their rationale and some examples. The results obtained are discussed and the errors analyzed.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th International Conference on Language Resources and Evaluation, LREC 2008
PublisherEuropean Language Resources Association (ELRA)
Pages310-313
Number of pages4
ISBN (Electronic)2951740840, 9782951740846
StatePublished - 2008
Externally publishedYes
Event6th International Conference on Language Resources and Evaluation, LREC 2008 - Marrakech, Morocco
Duration: May 28 2008May 30 2008

Publication series

NameProceedings of the 6th International Conference on Language Resources and Evaluation, LREC 2008

Conference

Conference6th International Conference on Language Resources and Evaluation, LREC 2008
Country/TerritoryMorocco
CityMarrakech
Period5/28/085/30/08

ASJC Scopus subject areas

  • Library and Information Sciences
  • Linguistics and Language
  • Language and Linguistics
  • Education

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