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.
CITATION STYLE
Zeng, D., Chen, Y., Liu, K., Zhao, J., & Lv, X. (2014). Learning the distinctive pattern space features for relation extraction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8801, 163–174. https://doi.org/10.1007/978-3-319-12277-9_15
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