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<Paper uid="P04-3022">
  <Title>Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Extracting Relations</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Extraction of semantic relationships between entities can be very useful for applications such as biography extraction and question answering, e.g.</Paragraph>
    <Paragraph position="1"> to answer queries such as &amp;quot;Where is the Taj Mahal?&amp;quot;. Several prior approaches to relation extraction have focused on using syntactic parse trees.</Paragraph>
    <Paragraph position="2"> For the Template Relations task of MUC-7, BBN researchers (Miller et al., 2000) augmented syntactic parse trees with semantic information corresponding to entities and relations and built generative models for the augmented trees. More recently, (Zelenko et al., 2003) have proposed extracting relations by computing kernel functions between parse trees and (Culotta and Sorensen, 2004) have extended this work to estimate kernel functions between augmented dependency trees.</Paragraph>
    <Paragraph position="3"> We build Maximum Entropy models for extracting relations that combine diverse lexical, syntactic and semantic features. Our results indicate that using a variety of information sources can result in improved recall and overall F measure. Our approach can easily scale to include more features from a multitude of sources-e.g. WordNet, gazatteers, output of other semantic taggers etc.-that can be brought to bear on this task. In this paper, we present our general approach, describe the features we currently use and show the results of our participation in the ACE evaluation.</Paragraph>
    <Paragraph position="4"> Automatic Content Extraction (ACE, 2004) is an evaluation conducted by NIST to measure Entity Detection and Tracking (EDT) and relation detection and characterization (RDC). The EDT task entails the detection of mentions of entities and chaining them together by identifying their coreference. In ACE vocabulary, entities are objects, mentions are references to them, and relations are explicitly or implicitly stated relationships among entities. Entities can be of five types: persons, organizations, locations, facilities, and geo-political entities (geographically defined regions that define a political boundary, e.g. countries, cities, etc.). Mentions have levels: they can be names, nominal expressions or pronouns.</Paragraph>
    <Paragraph position="5"> The RDC task detects implicit and explicit relations1 between entities identified by the EDT task.</Paragraph>
    <Paragraph position="6"> Here is an example:</Paragraph>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
The American Medical Association
</SectionTitle>
      <Paragraph position="0"> voted yesterday to install the heir apparent as its president-elect, rejecting a strong, upstart challenge by a District doctor who argued that the nation's largest physicians' group needs stronger ethics and new leadership.</Paragraph>
      <Paragraph position="1"> In electing Thomas R. Reardon, an Oregon general practitioner who had been the chairman of its board, ...</Paragraph>
      <Paragraph position="2"> In this fragment, all the underlined phrases are mentions referring to the American Medical Association, or to Thomas R. Reardon or the board (an organization) of the American Medical Association. Moreover, there is an explicit management relation between chairman and board, which are references to Thomas R. Reardon and the board of the American Medical Association respectively. Relation extraction is hard, since successful extraction implies correctly detecting both the argument mentions, correctly chaining these mentions to their re1Explict relations occur in text with explicit evidence suggesting the relationship. 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">  in the ACE 2003 evaluation.</Paragraph>
      <Paragraph position="4"> spective entities, and correctly determining the type of relation that holds between them.</Paragraph>
      <Paragraph position="5"> This paper focuses on the relation extraction component of our ACE system. The reader is referred to (Florian et al., 2004; Ittycheriah et al., 2003; Luo et al., 2004) for more details of our mention detection and mention chaining modules. In the next section, we describe our extraction system. We present results in section 3, and we conclude after making some general observations in section 4.</Paragraph>
    </Section>
  </Section>
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