Using Inductive Logic Programming for Natural Language Processing

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. 1 Inductive Logic Programming and Progol By using computational logic as the representational mechanism for hypotheses and observations, Inductive Logic Programming (ILP) can overcome the two main limitations of classical machine learning techniques, such as the Top-DownInduction -of-Decision-Tree (TDIDT) family [9]): 1. the use of a limited knowledge representation formalism (essentially a propositional logic), and 2. difficulties in using substantial background knowledge in the learning process. The first limitation..