Using Inductive Logic Programming for Natural Language Processing

James Cussens, David Page, Stephen Muggleton, Ashwin Srinivasan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We summarise recent work on using Inductive Logic Programming (ILP) for Natural Language Processing (NLP). ILP performs learning in a first-order logical setting, and is thus well-suited to induce over the various structured representations used in NLP. We present Stochastic Logic Programs (SLPs) and demonstrate their use in ILP when learning from positive examples only. We also give accounts of work on learning grammars from children's books and part-of-speech tagging.
Original languageEnglish
Title of host publicationECML'97 -- Workshop Notes on Empirical Learning of Natural Language Tasks
EditorsW. Daelemans, T. Weijters, A. van der Bosch
Place of PublicationPrague
PublisherUniversity of Economics, Prague
Number of pages10
Publication statusPublished - 1997

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