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.

• Type: Conference
• Year: 2010
• Where: APWEB
• Authors: Yulan Yan, Haibo Li, Yutaka Matsuo, Zhenglu Yang, Mitsuru Ishizuka
• Keywords: APWEB 2010, Binary Semantic Relation, Internet Technology, Relation Extraction Task, Semantic Relation Extraction