Text Patterns and Compression Models for Semantic Class Learning
Chung-Yao Chuang, Yi-Hsun Lee, Wen−Lian Hsu · 2011
This paper proposes a weakly-supervised approach for extracting instances of se-mantic classes. This method constructs simple wrappers automatically based on specified seed instances and uses a com-pression model to assess the contextual ev-idence of its extraction. By adopting this compression model, our approach can bet-ter avoid erroneous extractions in a noisy corpus such as the Web. The empiri-cal results show that our system performs quite consistently even when operating on a noisy text with a lot of possibly irrelevant documents. 1