Support Vector Machine Applied to the Semantic Interpretation of VN Compound
Jinglei Zhao, Hui Liu, Ruzhan Lu · 2007
The semantic interpretation of nominal compounds is one of the most difficult problems in natural language processing. VN compound is a subset of nominal compounds where the modifier is a verb nominalization. This paper proposes a new interpretation model in which a support vector machine is applied to label five semantic relations involved in Chinese VN compounds. The World Wide Web is exploited as a large corpus to compute point-wise mutual information between the VN compounds and a set of relation specific lexical patterns. Such Web-based statistics is used as the classification features for the support vector machine. By applying a sub-linear transformation and discretization of the raw statistics, a good result is obtained for the five semantic relations.