Causal Relation Extraction

Eduardo Blanco, Nuria Castell, Dan Moldovan


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.
Anthology ID:
L08-1175
Volume:
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)
Month:
May
Year:
2008
Address:
Marrakech, Morocco
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2008/pdf/87_paper.pdf
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http://www.lrec-conf.org/proceedings/lrec2008/pdf/87_paper.pdf