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
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2008/pdf/87_paper.pdf
- DOI:
- Cite (ACL):
- Eduardo Blanco, Nuria Castell, and Dan Moldovan. 2008. Causal Relation Extraction. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).
- Cite (Informal):
- Causal Relation Extraction (Blanco et al., LREC 2008)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2008/pdf/87_paper.pdf