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<Paper uid="P95-1038">
  <Title>Evaluation of Semantic Clusters</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction 1
</SectionTitle>
    <Paragraph position="0"> Most natural language processing (NLP) systems are designed to work on certain specific domains and porting them to other domains is often a very time-consuming and human-intenslve process. As the need for applying NLP systems to more and varied domains grows, it becomes increasingly important that some techniques be used to make these systems more portable. Several researchers (Lang and Hirschman, 1988; Rau et al., 1989; Pustejovsky, 1992; Grishman and Sterling, 1993; Basili et al., 1994), either directly or indirectly, have addressed issues that assist in making it easier to move an NLP system from one domain to another. One of the reasons for the lack of portability is the need for domain-specific semantic features that such systems often use for lexical, syntactic, and semantic disambiguation. One such feature is the knowledge of the semantic clusters in a domain.</Paragraph>
    <Paragraph position="1"> Since semantic classes are often domain-specific, their automatic acquisition is not trivial. Such classes can be derived either by distributional means or from existing taxonomies, knowledge bases, dictionaries, thesauruses, and so on. A prime example of the latter is WordNet which has been used to  inquiries should be addressed to rajeev@csc.ti.com.</Paragraph>
    <Paragraph position="2"> provide such semantic classes (Resnik, 1993; Basili et al., 1994) to assist in text understanding. Our efforts to obtain such semantic clusters with limited human intervention have been described elsewhere (Agarwal, 1995). This paper concentrates on the aspect of evahiating the obtained clusters against classes provided by human experts.</Paragraph>
  </Section>
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