Paper: Unsupervised Relation Disambiguation With Order Identification Capabilities

ACL ID W06-1667
Title Unsupervised Relation Disambiguation With Order Identification Capabilities
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

We present an unsupervised learning ap- proach to disambiguate various relations between name entities by use of various lexical and syntactic features from the contexts. It works by calculating eigen- vectors of an adjacency graph’s Lapla- cian to recover a submanifold of data from a high dimensionality space and then performing cluster number estima- tion on the eigenvectors. This method can address two difficulties encoutered in Hasegawa et al. (2004)’s hierarchical clustering: no consideration of manifold structure in data, and requirement to pro- vide cluster number by users. Experiment results on ACE corpora show that this spectral clustering based approach outper- forms Hasegawa et al. (2004)’s hierarchi- cal clustering method and a plain k-means clustering method.