Readings in natural language processing
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Automatic acquisition of subcategorization frames from untagged text
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Structural ambiguity and lexical relations
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
A computational mechanism for pronominal reference
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
A syntactic filter on pronominal anaphora for Slot Grammar
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
ACL '90 Proceedings of the 28th 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
Corpus-based acquisition of relative pronoun disambiguation heuristics
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
A binding rule for Government-binding parsing
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 1
Overview of the third message understanding evaluation and conference
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
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In this paper we show how a natural language system can learn to find the antecedents of relative pronouns. We use a well-known conceptual clustering system to create a case-based memory that predicts the antecedent of a wh-word given a description of the clause that precedes it. Our automated approach duplicates the performance of hand-coded rules. In addition, it requires only minimal syntactic parsing capabilities and a very general semantic feature set for describing nouns. Human intervention is needed only during the training phase. Thus, it is possible to compile relative pronoun disambiguation heuristics tuned to the syntactic and semantic preferences of a new domain with relative ease. Moreover, we believe that the technique provides a general approach for the automated acquisition of additional disambiguation heuristics for natural language systems, especially for problems that require the assimilation of syntactic and semantic knowledge.