Automatic Semantic Classification of Verbs From Their Syntactic Contexts: An Implemented Classifier for Stativity

AUTOMATIC SEMANTIC CLASSIFICATION OF VERBS FROM THEIR SYNTACTIC CONTEXTS: AN IMPLEMENTED CLASSIFIER FOR STATIVITY Michael R. Brent MIT AI Lab 545 Technology Square Cambridge, Massachusetts 02139 michael@ai.mit.edu Abstract This paper discusses an implemented program that automatically classifies verbs into those that ~ describe only states of the world, such as to know, and those that describe events, such as to look. It works by exploiting the con, straint between the syntactic environ- ments in which a verb can occur and its meaning. The only input is on-line text. This demonstrates an important new technique for the automatic gener- ation of lexical databases. 1 Introduction Young children and natural language process- ing programs face a common problem: everyone else knows a lot more about words. Children, it is hypothesized, catch up by observing the linguis- tic and non-linguistic contexts in which words are used. This paper focuses on the value and acces- sibility of the linguistic context. It argues that linguistic context by itself can provide useful cues about verb meaning to an artificial learner. This is demonstrated by a program that exploits two par- ticular cues from the linguistic context to classify verbs automatically into those whose sole sense is one describing a state, and those that have a sense describing an event. 1 The approach described here accounts for a certain degree of noise in the input due both to mis-apprehension of input sentences and to their occasional real.formation. This work shows that the two cues are available and are re- liable given the statistical methods applied. Language users, whether natural or artificial, need detailed semantic and syntactic classifica- tions of words. Ultimately, any artificial language IThe input sentences are those compiled in the Lan- caster/Oslo/Bergen (LOB) Corpus, a balanced corpus of one million words of British English. The LOB con- sists primarily of edited prose. user must be able to add new words to its lexicon, if only to accommodate the many neologisms it will encounter. And our lexicographic needs grow with our understanding of language. A number of current approaches to satisfying the lexical re- quirements for artificial devices do not involve un- supervised learning from examples. Boguraev and Briscoe (1987)discusses interpreting the informa- tion published in on-line dictionaries, while Zernik a/~d Dyer (1987) discuss tutored learning in a con. trolled environment. But any method that re- quires explicit human intervention -- be it that of lexicographers, knowledge engineers, or "tutors" -- will lag behind both the growth of vocabu- lary and the growth of linguistics, and even with the lag their maintenance will be expensive. By contrast, dictionaries constructed by automated learners from real sentences will not lag behind vocabulary growth; examples of current language use are free and nearly infinite. These observa- tions have led ~everal researchers, including Hindle (1990) and Smadja and McKeown (1990), to begin investigating automatic acquisition of semantics. Hindle and Smadja and McKeown rely purely on the ability of one particular word to statistically predict the occurrence of another in a …

Content truncated to 500 words