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<Paper uid="P05-1053">
  <Title>Exploring Various Knowledge in Relation Extraction</Title>
  <Section position="2" start_page="0" end_page="427" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> With the dramatic increase in the amount of textual information available in digital archives and the WWW, there has been growing interest in techniques 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 certain domain and put them in a structured format.</Paragraph>
    <Paragraph position="1"> 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 relations  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 &amp;quot;Who is the president of the United States?&amp;quot;.</Paragraph>
    <Paragraph position="2"> 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 the fact that most of relations defined in ACE corpus are within a very short distance. We also demonstrate how semantic information such as WordNet (Miller 1990) and Name List can be used in the feature-based framework. Evaluation shows that the incorporation of diverse features enables our system achieve best reported performance. It also shows that our fea- null In ACE (http://www.ldc.upenn.edu/Projects/ACE), explicit relations occur in text with explicit evidence suggesting the relationships. Implicit relations need not have explicit supporting evidence in text, though they should be evident from a reading of the document.</Paragraph>
    <Paragraph position="3">  ture-based approach outperforms tree kernel-based approaches by 11 F-measure in relation detection and more than 20 F-measure in relation detection and classification on the 5 ACE relation types.</Paragraph>
    <Paragraph position="4"> The rest of this paper is organized as follows.</Paragraph>
    <Paragraph position="5"> Section 2 presents related work. Section 3 and Section 4 describe our approach and various features employed respectively. Finally, we present experimental setting and results in Section 5 and conclude with some general observations in relation extraction in Section 6.</Paragraph>
  </Section>
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