This paper describes a case-based approach to knowledge acquisition for natural language systems that simultaneously learns part of speech, word sense, and concept activation knowledge for all open class words in a corpus. The parser begins with a lexicon of function words and creates a case base of context-sensitive word definitions during a humansupervised training phase. Then, given an unknownwordand the context in which it occurs, the parser retrieves definitions from the case base to infer the word's syntactic and semantic features. By encoding context as part of a definition, the meaning of a word can change dynamically in response to surrounding phrases without the need for explicit lexical disambiguation heuristics. Moreover, the approach acquires all three classes of knowledge using the same case representation and requires relatively little training and no hand-coded knowledge acquisition heuristics. We evaluate it in experiments that explore two of many practical applications of the technique and conclude that the case-based method provides a promising approach to automated dictionary construction and knowledge acquisition for sentence analysis in limited domains. In addition, we present a novel case retrieval algorithm that uses decision trees to improve the performance of a k-nearest neighbor similarity metric.

This paper describes and analyzes the results of the Third Message Understanding Conference (MUC-3). It reviews the purpose, history, and methodology of the conference, summarizes the participating systems, discusses issues of measuring system effectiveness, describes the linguistic phenomena tests, and provides a critical look at the evaluation in terms of the lessons learned. One of the common problems with evaluations is that the statistical significance of the results is unknown. In the discussion of system performance, the statistical significance of the evaluation results is reported and the use of approximate randomization to calculate the statistical significance of the results of MUC-3 is described

this paper have been performed on [AGRO]: a 1.3-million words French agronomy corpus and on [MEDIC]: a 1.56-million words English medical corpus. These corpus are composed of abstracts of scientific papers owned by INIST-CNRS.