Unsupervised Template Mining for Semantic Category Understanding

Abstract

We propose an unsupervised approach to constructing templates from a large collection of semantic category names, and use the templates as the semantic representation of categories. The main challenge is that many terms have multiple meanings, resulting in a lot of wrong templates. Statistical data and semantic knowledge are extracted from a web corpus to improve template generation. A nonlinear scoring function is proposed and demonstrated to be effective. Experiments show that our approach achieves significantly better results than baseline methods. As an immediate application, we apply the extracted templates to the cleaning of a category collection and see promising results (precision improved from 81% to 89%).