Incorporating linguistics constraints into inductive logic programming

Abstract

We report work on effectively incorporating linguistic knowledge into grammar induction. We use a highly interactive bottom-up inductive logic programming (ILP) algorithm to learn 'missing' grammar rules from an incomplete grammar. Using linguistic constraints on, for example, head features and gap threading, reduces the search space to such an extent that, in the small-scale experiments reported here, we can generate and store all candidate grammar rules together with information about their coverage and linguistic properties. This allows an appealingly simple and controlled method for generating linguistically plausible grammar rules. Starting from a base of highly specific rules, we apply least general generalisation and inverse resolution to generate more general rules. Induced rules are ordered, for example by coverage, for easy inspection by the user and at any point, the user can commit to a hypothesised rule and add it to the grammar. Related work in ILP and computational linguistics is discussed.