tailieunhanh - Báo cáo khoa học: "Acquisition of a Lexicon from Semantic Representations of Sentences*"

A system, WOLFIE, that acquires a mapping of words to their semantic representation is presented and a preliminary evaluation is performed. Tree least general generalizations (TLGGs) of the representations of input sentences are performed to assist in determining the representations of individual words in the sentences. The best guess for a meaning of a word is the T L G G which overlaps with the highest percentage of sentence representations in which that word appears. Some promising experimental results on a non-artificial data set are presented. . | Acquisition of a Lexicon from Semantic Representations of Sentences Cynthia A. Thompson Department of Computer Sciences University of Texas Taylor Hall Austin TX 78712 cthomp@ Abstract A system Wolfie that acquires a mapping of words to their semantic representation is presented and a preliminary evaluation is performed. Tree least general generalizations TLGGs of the representations of input sentences are performed to assist in determining the representations of individual 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 experimental results on a non-artificial data set are presented. 1 Introduction Computer language learning is an area of much potential and recent research. One goal is to learn to map surface sentences to a deeper semantic meaning. In the long term we would like to communicate with computers as easily as we do with people. Learning word meanings is an important step in this direction. Some other approaches to the lexical acquisition problem depend on knowledge of syntax 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 lexical acquisition system that learns a mapping of words to their semantic representation and which overcomes the above problems. Our system Wolfie WOrd Learning From Interpreted Examples learns this mapping from training examples consisting of sentences paired with their semantic 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