Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text
Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Part-of-Speech Tagging Using Progol
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Induction of Constraint Grammar-Rules Using Progol
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Learning for semantic interpretation: scaling up without dumbing down
Learning language in logic
ILP in part-of-speech tagging — an overview
Learning language in logic
Improving Part of Speech Disambiguation Rules by Adding Linguistic Knowledge
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Morphosyntactic Tagging of Slovene Using Progol
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Inductive improvement of part-of-speech tagging and its effect on a terminology of molecular biology
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
| Hi-index | 0.00 |
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-Umeå Corpus. Several thousand disambiguation rules were induced. When tested on unseen data, 98% of the words retained the correct reading after tagging. However, there were ambiguities pending after tagging, on an average 1.13 tags per word. The results suggest that the Progol system can be useful for learning tagging rules of good quality.