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Unsupervised Relation Disambiguation Using Spectral Clustering

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If you have a question about this talk, please contact Diarmuid Ó Séaghdha .

At this session of the NLIP Reading Group we’ll be discussing the following paper:

Jinxiu Chen, Donghong Ji, Chew Lim Tan and Zhengyu Niu. 2006. Unsupervised Relation Disambiguation Using Spectral Clustering. In Proceedings of the COLING /ACL 2006 Main Conference Poster Sessions.

Abstract: This paper presents 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. Experiment results on ACE corpora show that this spectral clustering based approach outperforms the other clustering methods.

This talk is part of the Natural Language Processing Reading Group series.

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