Using graph based method to improve bootstrapping relation extraction

Authors:
Haibo Li;Danushka Bollegala;Yutaka Matsuo;Mitsuru Ishizuka
Affiliations:
Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan;Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan;Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan;Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan
Venue:
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Year:
2011

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Abstract

Many bootstrapping relation extraction systems processing large corpus or working on the Web have been proposed in the literature. These systems usually return a large amount of extracted relationship instances as an out-of-ordered set. However, the returned result set often contains many irrelevant or weakly related instances. Ordering the extracted examples by their relevance to the given seeds is helpful to filter out irrelevant instances. Furthermore, ranking the extracted examples makes the selection of most similar instance easier. In this paper, we use a graph based method to rank the returned relation instances of a bootstrapping relation extraction system. We compare the used algorithm to the existing methods, relevant score based methods and frequency based methods, the results indicate that the proposed algorithm can improve the performance of the bootstrapping relation extraction systems.