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.
|Publisher||University of Economics, Prague|
|Number of pages||10|
|Place of Publication||Prague|
|Publication status||Published - 1997|