Learning to Disambiguate Relative Pronouns

Learning

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 10

Learning: Constructive and Linguistic

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.