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 Web-based 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 co-clustering 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-the-art clustering methods.