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<Paper uid="P06-1016">
  <Title>Modeling Commonality among Related Classes in Relation Extraction</Title>
  <Section position="3" start_page="0" end_page="121" 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) is such a technology that 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 ACE program (ACE 2000-2005), current research in IE has three main objectives: Entity Detection and Tracking (EDT), Relation Detection and Characterization (RDC), and Event Detection and Characterization (EDC). This paper will focus on the ACE RDC task, which detects and classifies various semantic relations between two entities. 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.</Paragraph>
    <Paragraph position="2"> to answer the query &amp;quot;Who is the president of the United States?&amp;quot;.</Paragraph>
    <Paragraph position="3"> One major challenge in relation extraction is due to the data sparseness problem (Zhou et al 2005). As the largest annotated corpus in relation extraction, the ACE RDC 2003 corpus shows that different subtypes/types of relations are much unevenly distributed and a few relation subtypes, such as the subtype &amp;quot;Founder&amp;quot; under the type &amp;quot;ROLE&amp;quot;, suffers from a small amount of annotated data. Further experimentation in this paper (please see Figure 2) shows that most relation subtypes suffer from the lack of the training data and fail to achieve steady performance given the current corpus size. Given the relative large size of this corpus, it will be time-consuming and very expensive to further expand the corpus with a reasonable gain in performance. Even if we can somehow expend the corpus and achieve steady performance on major relation subtypes, it will be still far beyond practice for those minor sub-types given the much unevenly distribution among different relation subtypes. While various machine learning approaches, such as generative modeling (Miller et al 2000), maximum entropy (Kambhatla 2004) and support vector machines (Zhao and Grisman 2005; Zhou et al 2005), have been applied in the relation extraction task, no explicit learning strategy is proposed to deal with the inherent data sparseness problem caused by the much uneven distribution among different relations.</Paragraph>
    <Paragraph position="4"> This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem by modeling the commonality among related classes. Through organizing various classes hierarchically, a linear discriminative function is determined for each class in a top-down way using a perceptron algorithm with the lower-level weight vector derived from the upper-level weight vector. Evaluation on the ACE RDC 2003 corpus shows that the hierarchical  strategy achieves much better performance than the flat strategy on least- and medium-frequent relations. It also shows that our system based on the hierarchical strategy outperforms the previous best-reported system.</Paragraph>
    <Paragraph position="5"> The rest of this paper is organized as follows.</Paragraph>
    <Paragraph position="6"> Section 2 presents related work. Section 3 describes the hierarchical learning strategy using the perceptron algorithm. Finally, we present experimentation in Section 4 and conclude this paper in Section 5.</Paragraph>
  </Section>
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