Causal Relation Extraction
Eduardo Blanco, Núria Castell, DAN I. MOLDOVAN · 2008
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.