Corpus-Based Linguistic Indicators for Aspectual Classification

Corpus-Based Linguistic Indicators for Aspectual Classification Eric V. Siegel Department of Computer Science Columbia University New York, NY 10027 Abstract Fourteen indicators that measure the frequency of lexico-syntactic phenomena linguistically re- lated to aspectual class are applied to aspec- tual classification. This group of indicators is shown to improve classification performance for two aspectual distinctions, stativity and com- pletedness (i.e., telicity), over unrestricted sets of verbs from two corpora. Several of these in- dicators have not previously been discovered to correlate with aspect. 1 Introduction Aspectual classification maps clauses to a small set of primitive categories in order to reason about time. For example, events such as, "You called your father," are distinguished from states such as, "You resemble your father." These two high-level categories correspond to primitive distinctions in many domains, e.g., the distinction between procedure and diagnosis in the medical domain. 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, "I made a fire" is culminated, since a new state is introduced - something is made, whereas, "I gazed at the sunset" is non-culminated. Aspectual classification is necessary for inter- preting temporal modifiers and assessing tem- poral entailments (Vendler, 1967; Dowty, 1979; Moens and Steedman, 1988; Dorr, 1992), and is therefore a necessary component for applica- tions that perform certain natural language in- terpretation, natural language generation, sum- marization, information retrieval, and machine translation tasks. Aspect introduces a large-scale, domain- dependent lexical classification problem. Al- though an aspectual lexicon of verbs would suf- fice to classify many clauses by their main verb only, a verb's primary class is often domain- dependent (Siegel, 1998b). Therefore, it is nec- essary to produce a specialized lexicon for each domain. Most approaches to automatically catego- rizing words measure co-occurrences between open-class lexical items (Schfitze, 1992; Hatzi- vassiloglou 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 se- mantic distinctions such as aspect. In this paper, we describe an expandable framework to classify verbs with linguistically- specialized numerical indicators. Each linguis- tic 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 clas- sification in particular. We perform an evalua- tion of fourteen linguistic indicators over unre- stricted 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 com- pletedness. Moreover, our analysis reveals a predictive value for several indicators that have not previously been discovered to correlate with aspect in the linguistics literature. The following section further describes as- pect, and introduces linguistic insights that are exploited by linguistic indicators. The next sec- tion describes the set of linguistic indicators evaluated in this paper. Then, our experimen- tal method and results …

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