Exploring Various Knowledge in Relation Extraction

Proceedings of the 43rd Annual Meeting of the ACL, pages 427–434, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Exploring Various Knowledge in Relation Extraction ZHOU GuoDong SU Jian ZHANG Jie ZHANG Min Institute for Infocomm research 21 Heng Mui Keng Terrace, Singapore 119613 Email: {zhougd, sujian, zhangjie, mzhang}@i2r.a-star.edu.sg Abstract Extracting semantic relationships between en- tities is challenging. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based rela- tion extraction using SVM. Our study illus- trates that the base phrase chunking information is very effective for relation ex- traction and contributes to most of the per- formance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement. This sug- gests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. We also demonstrate how semantic information such as WordNet and Name List, can be used in fea- ture-based relation extraction to further im- prove the performance. Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outper- form previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types. 1 Introduction With the dramatic increase in the amount of textual information available in digital archives and the WWW, there has been growing interest in tech- niques for automatically extracting information from text. Information Extraction (IE) systems are expected to identify relevant information (usually of pre-defined types) from text documents in a cer- tain domain and put them in a structured format. According to the scope of the NIST Automatic Content Extraction (ACE) program, current research in IE has three main objectives: Entity Detection and Tracking (EDT), Relation Detection and Characterization (RDC), and Event Detection and Characterization (EDC). The EDT task entails the detection of entity mentions and chaining them together by identifying their coreference. In ACE vocabulary, entities are objects, mentions are references to them, and relations are semantic relationships between entities. Entities can be of five types: persons, organizations, locations, facilities and geo-political entities (GPE: geographically defined regions that indicate a political boundary, e.g. countries, states, cities, etc.). Mentions have three levels: names, nomial expressions or pronouns. The RDC task detects and classifies implicit and explicit relations1 between entities identified by the EDT task. For example, we want to determine whether a person is at a location, based on the evidence in the context. Extraction of semantic relationships between entities can be very useful for applications such as question answering, e.g. to answer the query “Who is the president of the United States?”. This paper focuses on the ACE RDC task and employs diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using Support Vector Machines (SVMs). Our study illustrates that the base phrase chunking information contributes to most of the performance inprovement from syntactic aspect while additional full parsing information does not contribute much, largely due to …

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