Deriving Verbal and Compositonal Lexical Aspect for NLP Applications

Deriving Verbal and Compositional Lexical Aspect for NLP Applications Bonnie J. Dorr and Marl Broman Olsen University of Maryland Institute for Ad.vanced Computer Studies A.V. Williams Building College Park, MD 20742, USA bonnie ,molsen©umiacs. umd. edu Abstract Verbal and compositional lexical aspect provide the underlying temporal struc- ture of events. Knowledge of lexical as- pect, e.g., (a)telicity, is therefore required for interpreting event sequences in dis- course (Dowty, 1986; Moens and Steed- man, 1988; Passoneau, 1988), interfacing to temporal databases (Androutsopoulos, 1996), processing temporal modifiers (An- tonisse, 1994), describing allowable alter- nations and their semantic effects (Resnik, 1996; Tenny, 1994), and selecting tense and lexical items for natural language gen- eration ((Dorr and Olsen, 1996; Klavans and Chodorow, 1992), cf. (Slobin and Bo- caz, 1988)). We show that it is possible to represent lexical aspect--both verbal and compositional--on a large scale, us- ing Lexical Conceptual Structure (LCS) representations of verbs in the classes cat- aloged by Levin (1993). We show how proper consideration of these universal pieces of verb meaning may be used to refine lexical representations and derive a range of meanings from combinations of LCS representations. A single algorithm may therefore be used to determine lexical aspect classes and features at both verbal and sentence levels. Finally, we illustrate how knowledge of lexical aspect facilitates the interpretation of events in NLP appli- cations. 1 Introduction Knowledge of lexical aspect--how verbs denote situ- ations 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 (Androutsopou- los, 1996), processing temporal modifiers (Antonisse, 1994), describing allowable alternations and their se- mantic 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 sensi- tive 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. with aspectual properties of verbs clearly influenc- ing the alternations of interest. He also points out that these properties are difficult to obtain directly from corpora. The ability to determine lexical aspect, on a large scale and in the sentential context, therefore yields an important source of constraints for corpus anal- ysis and psycholinguistic experimentation, as well as for NLP applications such as machine transla- tion (Dorr et al., 1995b) and foreign language tu- toring (Dorr et al., 1995a; Sams. 1995; Weinberg et al., 1995). Other researchers have proposed corpus- based approaches to acquiring lexical aspect infor- mation with varying data coverage: Klavans and Chodorow (1992) focus on the event-state distinc- tion 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. properties. Conversely. a number of works in …

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