Paper: Unsupervised Relation Disambiguation Using Spectral Clustering

ACL ID P06-2012
Title Unsupervised Relation Disambiguation Using Spectral Clustering
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

This paper presents an unsupervised learn- ing approach to disambiguate various rela- tions between name entities by use of vari- ous lexical and syntactic features from the contexts. It works by calculating eigen- vectors of an adjacency graph’s Laplacian to recover a submanifold of data from a high dimensionality space and then per- forming cluster number estimation on the eigenvectors. Experiment results on ACE corpora show that this spectral cluster- ing based approach outperforms the other clustering methods.