Improving Scalability of Inductive Logic Programming via Pruning and
  Best-Effort Optimisation
  Inductive Logic Programming (ILP) combines rule-based and statistical
artificial intelligence methods, by learning a hypothesis comprising a set of
rules given background knowledge and constraints for the search space. We focus
on extending the XHAIL algorithm for ILP which is based on Answer Set
Programming and we evaluate our extensions using the Natural Language
Processing application of sentence chunking. With respect to processing natural
language, ILP can cater for the constant change in how we use language on a
daily basis. At the same time, ILP does not require huge amounts of training
examples such as other statistical methods and produces interpretable results,
that means a set of rules, which can be analysed and tweaked if necessary. As
contributions we extend XHAIL with (i) a pruning mechanism within the
hypothesis generalisation algorithm which enables learning from larger
datasets, (ii) a better usage of modern solver technology using recently
developed optimisation methods, and (iii) a time budget that permits the usage
of suboptimal results. We evaluate these improvements on the task of sentence
chunking using three datasets from a recent SemEval competition. Results show
that our improvements allow for learning on bigger datasets with results that
are of similar quality to state-of-the-art systems on the same task. Moreover,
we compare the hypotheses obtained on datasets to gain insights on the
structure of each dataset.
