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<Paper uid="P97-1020">
  <Title>Deriving Verbal and Compositional Lexical Aspect for NLP Applications</Title>
  <Section position="3" start_page="0" end_page="151" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Knowledge of lexical aspect--how verbs denote situations as developing or holding in time--is required for interpreting event sequences in discourse (Dowty, 1986; Moens and Steedman, 1988; Passoneau, 1988), interfacing to temporal databases (Androutsopoulos, 1996), processing temporal modifiers (Antonisse, 1994), describing allowable alternations and their semantic effects (Resnik, 1996; Tenny, 1994), and for selecting tense and lexical items for natural language generation ((Dorr and Olsen. 1996: Klavans and Chodorow, 1992), cf. (Slobin and Bocaz, 1988)). In addition, preliminary pyscholinguistic experiments (Antonisse, 1994) indicate that subjects are sensitive to the presence or absence of aspectual features when processing temporal modifiers. Resnik (1996) showed that the strength of distributionally derived selectional constraints helps predict whether verbs can participate in a class of diathesis alternations.</Paragraph>
    <Paragraph position="1"> with aspectual properties of verbs clearly influencing the alternations of interest. He also points out that these properties are difficult to obtain directly from corpora.</Paragraph>
    <Paragraph position="2"> The ability to determine lexical aspect, on a large scale and in the sentential context, therefore yields an important source of constraints for corpus analysis and psycholinguistic experimentation, as well as for NLP applications such as machine translation (Dorr et al., 1995b) and foreign language tutoring (Dorr et al., 1995a; Sams. 1995; Weinberg et al., 1995). Other researchers have proposed corpus-based approaches to acquiring lexical aspect information with varying data coverage: Klavans and Chodorow (1992) focus on the event-state distinction in verbs and predicates; Light (1996) considers the aspectual properties of verbs and affixes; and McKeown and Siegel (1996) describe an algorithm for classifying sentences according to lexical aspect.</Paragraph>
    <Paragraph position="3"> properties. Conversely. a number of works in the linguistics literature have proposed lexical semantic templates for representing the aspectual properties of verbs (Dowry, 1979: Hovav and Levin, 1995; Levin and Rappaport Hovav. To appear), although these have not been implemented and tested on a large scale.</Paragraph>
    <Paragraph position="4"> We show that. it is possible to represent the lexical aspect both of verbs alone and in sentential contexts using Lexical Conceptual Structure (LCS) representations of verbs in the classes cataloged by Levin (1993). We show how proper consideration of these universal pieces of verb meaning may be used t.o refine lexical representations and derive a range of meanings from combinations of LCS representations.</Paragraph>
    <Paragraph position="5">  A single algorithm may therefore be used to determine lexical aspect classes and features at both verbal and sentential levels. Finally, we illustrate how access to lexical aspect facilitates lexical selection and the interpretation of events in machine translation and foreign language tutoring applications, respectively. null</Paragraph>
  </Section>
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