Paper: Learning Constraint Grammar-style Disambiguation Rules using Inductive Logic Programming

ACL ID P98-2128
Title Learning Constraint Grammar-style Disambiguation Rules using Inductive Logic Programming
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998
Authors

This paper reports a pilot study, in which Constraint Grammar inspired rules were learnt using the Progol machine-learning system. Rules discarding faulty readings of ambiguously tagged words were learnt for the part of speech tags of the Stockholm-Ume£ Corpus. Several thousand disambiguation rules were induced. When tested on unseen data, 98% of the words retained the correct reading after tagging. How- ever, there were ambiguities pending after tag- ging, on an average 1.13 tags per word. The results suggest that the Progol system can be useful for learning tagging rules of good qual- ity.