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<Paper uid="P99-1015">
  <Title>Corpus-Based Linguistic Indicators for Aspectual Classification</Title>
  <Section position="2" start_page="0" end_page="112" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Aspectual classification maps clauses to a small set of primitive categories in order to reason about time. For example, events such as, &amp;quot;You called your father,&amp;quot; are distinguished from states such as, &amp;quot;You resemble your father.&amp;quot; These two high-level categories correspond to primitive distinctions in many domains, e.g., the distinction between procedure and diagnosis in the medical domain.</Paragraph>
    <Paragraph position="1"> Aspectual classification further distinguishes events according to completedness (i.e., telicity), which determines whether an event reaches a culmination point in time at which a new state is introduced. For example, &amp;quot;I made a fire&amp;quot; is culminated, since a new state is introduced - something is made, whereas, &amp;quot;I gazed at the sunset&amp;quot; is non-culminated.</Paragraph>
    <Paragraph position="2"> Aspectual classification is necessary for interpreting temporal modifiers and assessing temporal entailments (Vendler, 1967; Dowty, 1979; Moens and Steedman, 1988; Dorr, 1992), and is therefore a necessary component for applications that perform certain natural language interpretation, natural language generation, summarization, information retrieval, and machine translation tasks.</Paragraph>
    <Paragraph position="3"> Aspect introduces a large-scale, domain-dependent lexical classification problem. Although an aspectual lexicon of verbs would suffice to classify many clauses by their main verb only, a verb's primary class is often domain-dependent (Siegel, 1998b). Therefore, it is necessary to produce a specialized lexicon for each domain.</Paragraph>
    <Paragraph position="4"> Most approaches to automatically categorizing words measure co-occurrences between open-class lexical items (Schfitze, 1992; Hatzivassiloglou and McKeown, 1993; Pereira et al., 1993). This approach is limited since co-occurrences between open-class lexical items is sparse, and is not specialized for particular semantic distinctions such as aspect.</Paragraph>
    <Paragraph position="5"> In this paper, we describe an expandable framework to classify verbs with linguisticallyspecialized numerical indicators. Each linguistic indicator measures the frequency of a lexico-syntactic marker, e.g. the perfect tense. These markers are linguistically related to aspect, so the indicators are specialized for aspectual classification in particular. We perform an evaluation of fourteen linguistic indicators over unrestricted sets of verbs from two corpora. When used in combination, this group of indicators is shown to improve classification performance for two aspectual distinctions: stativity and completedness. Moreover, our analysis reveals a predictive value for several indicators that have not previously been discovered to correlate with aspect in the linguistics literature.</Paragraph>
    <Paragraph position="6"> The following section further describes aspect, and introduces linguistic insights that are exploited by linguistic indicators. The next section describes the set of linguistic indicators evaluated in this paper. Then, our experimental method and results are given, followed by a discussion and conclusions.</Paragraph>
    <Paragraph position="7">  Table 1 summarizes the three aspectual distinctions, which compose five aspectual categories. In addition to the two distinctions described in the previous section, atomicity distinguishes events according to whether they have a time duration (punctual versus extended). Therefore, four classes of events are derived: culmination, culminated process, process, and point. These aspectual distinctions are defined formally by Dowty (1979).</Paragraph>
    <Paragraph position="8"> Several researchers have developed models that incorporate aspectual class to assess temporal constraints between clauses (Passonneau,  1988; Dorr, 1992). For example, stativity must be identified to detect temporal constraints between clauses connected with when, e.g., in interpreting (1), (1) She had good strength when objectively tested.</Paragraph>
    <Paragraph position="9"> the following temporal relationship holds:</Paragraph>
    <Paragraph position="11"> However, in interpreting (2), (2) Phototherapy was discontinued when the bilirubin came down to 13.</Paragraph>
    <Paragraph position="12"> the temporal relationship is different: COme I I discontinue I I These aspectual distinctions are motivated by a series of entailment constraints. In particular, certain lexico-syntactic features of a clause, such as temporal adjuncts and tense, are constrained by and contribute to the aspectual class of the clause (Vendler, 1967; Dowty, 1979). Tables 2 illustrates an array of linguistic con- null constraints on aspectual class, primarily from Klavans' summary (1994).</Paragraph>
    <Paragraph position="13"> If a clause can occur: then it is: with a temporal adverb Event (e.g., then) in progressive Extended</Paragraph>
    <Section position="1" start_page="112" end_page="112" type="sub_section">
      <SectionTitle>
Event
</SectionTitle>
      <Paragraph position="0"> with a duration in-PP Culm Event (e.g., in an hour) in the perfect tense Culm Event or State straints. Each entry in this table describes an aspectual marker and the constraints on the aspectual category of any clause that appears with that marker. For example, a clause must be an extended event to appear in the progressive tense, e.g.,  (3) He was prospering in India. (extended), which contrasts with, (4) *You were noticing it. (punctual). and, (5) *She was seeming sad. (state). As a second example, an event must be culminated to appear in the perfect tense, for example, null (6) She had made an attempt. (culm.), which contrasts with, (7) *He has cowered down. (non-culm.)</Paragraph>
    </Section>
  </Section>
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