Unsupervised relation disambiguation with order identification capabilities

Abstract

We present an unsupervised learning approach to disambiguate various relations between name entities by use of various lexical and syntactic features from the contexts. It works by calculating eigenvectors of an adjacency graph's Laplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation 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 provide cluster number by users. Experiment results on ACE corpora show that this spectral clustering based approach outperforms Hasegawa et al. (2004)'s hierarchical clustering method and a plain k-means clustering method. © 2006 Association for Computational Linguistics.