A computer model of child language learning
Artificial Intelligence
Word association norms, mutual information, and lexicography
Computational Linguistics
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Extending the Lexicon by Exploiting Subregularities
Extending the Lexicon by Exploiting Subregularities
Automatic acquisition of subcategorization frames from untagged text
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Noun classification from predicate-argument structures
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
SEXTANT: exploring unexplored contexts for semantic extraction from syntactic analysis
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
A class-based approach to lexical discovery
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
University of Massachusetts: MUC-3 test results and analysis
MUC3 '91 Proceedings of the 3rd conference on Message understanding
University of Massachusetts: description of the CIRCUS system as used for MUC-3
MUC3 '91 Proceedings of the 3rd conference on Message understanding
Learning word meanings from examples
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
A program that figures out meanings of words from context
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Case retrieval nets for heuristic lexicalization in natural language generation
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
An overview and classification of adaptive approaches to information extraction
Journal on Data Semantics IV
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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 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-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.