Incorporating Linguistics Constraints into Inductive Logic Programming

We report work on effectively incorporating lin-
guistic 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, re-
duces the search space to such an extent that,
in the small-scale experiments reported here,
we can generate and store all candidate gram-
mar 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 spe-
cific 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 hypothe-
sised rule and add it to the grammar. Related
work in ILP and computational linguistics is
discussed