An Eeffective Methods of Using Web Based Information for Relation Extraction.
Stanley Wai Keong Yong, Jian Su · International Joint Conference on Natural Language Processing · 2008
We propose a method that incorporates paraphrase information from the Web to boost the performance of a supervised relation extraction system. Contextual information is extracted from the Web using a semi-supervised process, and summarized by skip-bigram overlap measures over the entire extract. This allows the capture of local contextual information as well as more distant associations. We observe a statistically significant boost in relation extraction performance. We investigate two extensions, thematic clustering and hypernym expansion. In tandem with thematic clustering to reduce noise in our paraphrase extraction, we attempt to increase the coverage of our search for paraphrases using hypernym expansion. Evaluation of our method on the ACE 2004 corpus shows that it out-performs the baseline SVM-based supervised learning algorithm across almost all major ACE relation types, by a margin of up to 31%.