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<Paper uid="C96-2159">
  <Title>Decision Tree Learning Algorithm with Structured Attributes: Application to Verbal Case Frame Acquisition</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The group of Decision Tree Learning Algorithms (DTLAs) like CART (Breiman et al., 1984), ID3 (Quinlan, 1986) and C4.5 (Quinlan, 1993) are some of the most widely used algorithms for learning the rules for expert systems and has been sueeessfully applied to several areas so far.</Paragraph>
    <Paragraph position="1"> These algorithms are now getting keen attention from the natural language processing (NLP) research community since the huge text corpus is becoming widely available. The most popular touchstone for the DTLA in this community is the verbal case frame or the translation rules. There have already been some attempts, like (Tanaka, 1994) and (Almuallim et al., 1994).</Paragraph>
    <Paragraph position="2"> The group of DTLAs, however, was originally designed to handle &amp;quot;plain&amp;quot; data, whereas nouns are &amp;quot;structured&amp;quot; under a thesaurus. Although handling such &amp;quot;structured attributes&amp;quot; in the DTLA was described as a &amp;quot;desirable extension&amp;quot; in the book of Quinlan (Quinlan, 1993), the  tanakah@strl, nhk. or. jp value attribute (case) (semantic restriction) object noun ,.t \[Y= I I Q l \[ I Taro Hanako cat dog elephant TV camera tsurete-iku \[escort\] hakobu\[carry\]  problem has received rather limited attention so far (Ahnuallim et el., 1995).</Paragraph>
    <Paragraph position="3"> There have been several attempts to solve tile problem in the NLP community, such as (Tanaka, 1995b), (Almuallim et el., 1995). These attempts, however, are not always satisfactory in that the handling of the thesaurus is not flexible enough. In this paper, we introduce an extended DTLA, LASA-1 (inductive earning Algorithm with Structured Attributes), which can handle structured attributes in an optimmn way. We first present an algorithm called T*, which can solve the sub-problem for structured attributes and then present the whole algorithm of LASA-1. Finally, we report an application of our new algorithm to verbal case frame acquisition and show its effectiveness. null</Paragraph>
  </Section>
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