Acquisition of a Lexicon from Semantic Representations of Sentences

Acquisition of a Lexicon from Semantic Representations of Sentences* Cynthia A. Thompson Department of Computer Sciences University of Texas 2.124 Taylor Hall Austin, TX 78712 cthomp@cs.utexas.edu Abstract A system, WOLFIE, that acquires a map- ping of words to their semantic representa- tion is presented and a preliminary evalua- tion is performed. Tree least general gener- alizations (TLGGs) of the representations of input sentences are performed to assist in determining the representations of indi- vidual words in the sentences. The best guess for a meaning of a word is the TLGG which overlaps with the highest percentage of sentence representations in which that word appears. Some promising experimen- tal results on a non-artificial data set are presented. 1 Introduction Computer language learning is an area of much po- tential and recent research. One goal is to learn to map surface sentences to a deeper semantic mean- ing. In the long term, we would like to communi- cate with computers as easily as we do with peo- ple. Learning word meanings is an important step in this direction. Some other approaches to the lexi- cal acquisition problem depend on knowledge of syn- tax to assist in lexical learning (Berwick and Pilato, 1987). Also, most of these have not demonstrated the ability to tie in to the rest of a language learning system (Hastings and Lytinen, 1994; Kazman, 1990; Siskind, 1994). Finally, unnatural data is sometimes needed (Siskind, 1994). We present a lexicM acquisition system that learns a mapping of words to their semantic representa- tion, and which overcomes the above problems. Our system, WOLFIE (WOrd Learning From Interpreted Examples), learns this mapping from training ex- amples consisting of sentences paired with their se- mantic representation. The representation used here is based on Conceptual Dependency (CD) (Schank, 1975). The results of our system can be used to *This research was supported by the National Science Foundation under grant IRI-9310819 assist a larger language acquisition system; in par- ticular, we use the results as part of the input to CHILL (Zelle and Mooney, 1993). CHILL learns to parse sentences into case-role representations by an- Myzing a sample of sentence/case-role pairings. By extending the representation of each word to a CD representation, the problem faced by CHILL is made more difficult. Our hypothesis is that the output from WOLFIE can ease the difficulty. In the long run, a system such as WOLFIE could be used to help learn to process natural language queries and translate them into a database query language. Also, WOLFIE could possibly assist in translation from one natural language to another. 2 Problem Definition and Algorithm 2.1 The Lexical Learning Problem Given: A set of sentences, S paired with represen- tations, R. Find: A pairing of a subset of the words, W in S with representations of those words. Some sentences can have multiple representations because of ambiguity, both at the word and sentence level. The representations for a word are formed from subsets of the representations of input sen- tences in …

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