Morphological Cues for Lexical Semantics

Morphological Cues for Lexical Semantics Marc Light Seminar ffir Sprachwissenschaft Universitgt Tfibingen Wilhelmstr. 113 D-72074 Tfibingen Germany light~sf s. nph±l, uni-tuebingen, de Abstract Most natural language processing tasks re- quire lexical semantic information. Au- tomated acquisition of this information would thus increase the robustness and portability of NLP systems. This pa- per describes an acquisition method which makes use of fixed correspondences be- tween derivational affixes and lexical se- mantic information. One advantage of this method, and of other methods that rely only on surface characteristics of language, is that the necessary input is currently available. 1 Introduction Some natural language processing (NLP) tasks can be performed with only coarse-grained semantic in- formation about individual words. For example, a system could utilize word frequency and a word cooccurrence matrix in order to perform informa- tion retrieval. However, many NLP tasks require at least a partial understanding of every sentence or utterance in the input and thus have a much greater need for lexical semantics. Natural language gen- eration, providing a natural language front end to a database, information extraction, machine trans- lation, and task-oriented dialogue understanding all require lexical semantics. The lexical semantic in- formation commonly utilized includes verbal argu- ment structure and selectional restrictions, corre- sponding nominal semantic class, verbal aspectual class, synonym and antonym relationships between words, and various verbal semantic features such as causation and manner. Machine readable dictionaries do not include much of this information and it is difficult and time consuming to encode it by hand. As a consequence, current NLP systems have only small lexicons and thus can only operate in restricted domains. Auto- mated methods for acquiring lexical semantics could increase both the robustness and the portability of such systems. In addition, such methods might pro- vide inSight into human language acquisition. After considering different possible approaches to acquiring lexicM semantic information, this paper concludes that a "surface cueing" approach is cur- rently the most promising. It then introduces mor- phological cueing, a type of surface cueing, and dis- cusses an implementation. It concludes by evalu- ating morphological cues with respect to a list of desiderata for good surface cues. 2 Approaches to Acquiring Lexical Semantics One intuitively appealing idea is that humans ac- quire the meanings of words by relating them to semantic representations resulting from perceptual or cognitive processing. For example, in a situation where the father says Kim is throwing the ball and points at Kim who is throwing the ball, a child might be able learn what throw and ball mean. In the human language acquisition literature, Grimshaw (1981) and Pinker (1989) advocate this approach; others have described partial computer implementa- tions: Pustejovsky (1988) and Siskind (1990). How- ever, this approach cannot yet provide for the auto- matic acquisition of lexical semantics for use in NLP systems, because the input required must be hand coded: no current artificial intelligence system has the perceptual and cognitive capabilities required to produce the needed semantic representations. Another approach would be to use …

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