Unsupervised Feature Selection for Relation Extraction

Abstract

This paper presents an unsupervised relation extraction algorithm, which induces relations between entity pairs by grouping them into a “natural” number of clusters based on the similarity of their contexts. Stability-based criterion is used to automatically estimate the number of clusters. For removing noisy feature words in clustering procedure, feature selection is conducted by optimizing a trace based criterion subject to some constraint in an unsupervised manner. After relation clustering procedure, we employ a discriminative category matching (DCM) to find typical and discriminative words to represent different relations. Experimental results show the effectiveness of our algorithm.