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Relation extraction is the task of finding semantic relations between two entities from text. In this paper, we propose a novel feature-based Chinese relation extraction approach that explicitly defines and explores nine positional structures between two entities. We also suggest some correction and inference mechanisms based on relation hierarchy and co-reference information etc. The approach is effective when evaluated on the ACE 2005 Chinese data set. | A Novel Feature-based Approach to Chinese Entity Relation Extraction Wenjie Li1 Peng Zhang1 2 Furu Wei1 Yuexian Hou2 and Qin Lu1 Department of Computing 2School of Computer Science and Technology The Hong Kong Polytechnic University Hong Kong Tianjin University China cswjli csfwei csluqin @comp.polyu.edu.hk pzhang yxhou @tju.edu.cn Abstract Relation extraction is the task of finding semantic relations between two entities from text. In this paper we propose a novel feature-based Chinese relation extraction approach that explicitly defines and explores nine positional structures between two entities. We also suggest some correction and inference mechanisms based on relation hierarchy and co-reference information etc. The approach is effective when evaluated on the ACE 2005 Chinese data set. 1 Introduction Relation extraction is promoted by the ACE program. It is the task of finding predefined semantic relations between two entities from text. For example the sentence Bill Gates is the chairman and chief software architect of Microsoft Corporation conveys the ACE-style relation OrG-AFFILIATIoN between the two entities Bill Gates PER and Microsoft Corporation ORG The task of relation extraction has been extensively studied in English over the past years. It is typically cast as a classification problem. Existing approaches include feature-based and kernel-based classification. Feature-based approaches transform the context of two entities into a liner vector of carefully selected linguistic features varying from entity semantic information to lexical and syntactic features of the context. Kernel-based approaches on the other hand explore structured representation such as parse tree and dependency tree and directly compute the similarity between trees. Comparably feature-based approaches are easier to implement and achieve much success. In contrast to the significant achievements concerning English and other Western languages research progress in Chinese relation .
