Relation extraction pattern ranking using word similarity

Our thesis proposal aims at integrating word similarity measures in pattern ranking for relation extraction bootstrapping algorithms. We note that although many contributions have been done on pattern ranking schemas, few explored the use of word-level semantic similarity. Our hypothesis is that word similarity would allow better pattern comparison and better pattern ranking, resulting in less semantic drift commonly problematic in bootstrapping algorithms. In this paper, as a first step into this research, we explore different pattern representations, various existing pattern ranking approaches and some word similarity measures. We also present a methodology and evaluation approach to test our hypothesis.