Automatically acquiring models of preposition use

Authors:
Rachele De Felice;Stephen G. Pulman
Affiliations:
Oxford University Computing Laboratory, Oxford, UK;Oxford University Computing Laboratory, Oxford, UK
Venue:
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Year:
2007

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Abstract

This paper proposes a machine-learning based approach to predict accurately, given a syntactic and semantic context, which preposition is most likely to occur in that context. Each occurrence of a preposition in an English corpus has its context represented by a vector containing 307 features. The vectors are processed by a voted perceptron algorithm to learn associations between contexts and prepositions. In preliminary tests, we can associate contexts and prepositions with a success rate of up to 84.5%.