Semantic Web Dog Food

Presented at: The Sixth International Language Resources and Evaluation Conference (LREC2008)

by Eduardo Blanco, Nuria Castell, Dan Moldovan

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.

Keywords: Acquisition, Machine Learning, Information Extraction, Information Retrieval, Semantics, Linguistics

Resource URI on the dog food server: http://data.semanticweb.org/conference/lrec/2008/papers/87

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