Abstract
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.
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Takamatsu, S., Sato, I., Nakagawa, H. (2011). Probabilistic Matrix Factorization Leveraging Contexts for Unsupervised Relation Extraction. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20841-6_8
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DOI: https://doi.org/10.1007/978-3-642-20841-6_8
Publisher Name: Springer, Berlin, Heidelberg
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