Learning the Distinctive Pattern Space Features for Relation Extraction

Daojian Zeng, Yubo Chen, Kang Liu, Jun Zhao, Xueqiang Lv

Abstract: Recently, Distant Supervision (DS) is used to automatically generate training data for relation extraction. As the vast redundancy of information on the web, multiple sentences corresponding to a fact may be achieved. In this paper, we propose pattern space features to leverage data redundancy. Each dimension of pattern space feature vector corresponds to a basis pattern and the vector value is the similarity of entity pairs’ patterns to basis patterns. To achieve distinctive basis patterns, a pattern selection procedure is adopted to filter out noisy patterns. In addition, since too specific patterns will increase the number of basis patterns, we propose a novel pattern extraction method that can avoid extracting too specific patterns while maintaining pattern distinctiveness. To demonstrate the effectiveness of the proposed features, we conduct the experiments on a real world data set with 6 different relation types. Experimental results demonstrate that pattern space features significantly outperform State-of-the-art.

External IDs:dblp:conf/cncl/ZengCLZL14