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 human-supervised training phase. Then, given an unknown word and 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-basedmethod 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.