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APWEB
2010
Springer

Multi-View Clustering with Web and Linguistic Features for Relation Extraction

9 years 2 months ago
Multi-View Clustering with Web and Linguistic Features for Relation Extraction
Binary semantic relation extraction is particularly useful for various NLP and Web applications. Currently Webbased methods and Linguistic-based methods are two types of leading methods for semantic relation extraction task. With a novel view on integrating linguistic analysis on local text with Web frequent information, we propose a multi-view coclustering approach for semantic relation extraction. One is feature clustering by automatically learning clustering functions for Web features, linguistic features simultaneously based on a subset of entity pairs. The other is relation clustering, using the feature clustering functions to define learning function for relation extraction. Our experiments demonstrate the superiority of our clustering approach comparing with several state-of-theart clustering methods.
Yulan Yan, Haibo Li, Yutaka Matsuo, Zhenglu Yang,
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where APWEB
Authors Yulan Yan, Haibo Li, Yutaka Matsuo, Zhenglu Yang, Mitsuru Ishizuka
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