Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
URES: an unsupervised web relation extraction system
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Unsupervised methods for determining object and relation synonyms on the web
Journal of Artificial Intelligence Research
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Measuring semantic similarity by latent relational analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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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.