Probabilistic Matrix Factorization Leveraging Contexts for Unsupervised Relation Extraction

摘要

The clustering of the semantic relations between entities extracted from a corpus is one of the main issues in unsupervised relation extraction (URE). Previous methods assume a huge corpus because they have utilized frequently appearing entity pairs in the corpus. In this paper, we present a URE that works well for a small corpus by using word sequences extracted as relations. The feature vectors of the word sequences are extremely sparse. To deal with the sparseness problem, we take the two approaches: dimension reduction and leveraging context in the whole corpus including sentences from which no relations are extracted. The context in this case is captured with feature co-occurrences, which indicate appearances of two features in a single sentence. The approaches are implemented by a probabilistic matrix factorization that jointly factorizes the matrix of the feature vectors and the matrix of the feature co-occurrences. Experimental results show that our method outperforms previously proposed methods.