Machine Learning
Part-of-Speech Tagging Using Progol
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Learning Constraint Grammar-style disambiguation rules using inductive logic programming
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Top-down induction of first-order logical decision trees
Artificial Intelligence
Solving Selection Problems Using Preference Relation Based on Bayesian Learning
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Inductive Logic Programming
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We consider the task of tagging Slovene words with morpho-syntactic descriptions (MSDs). MSDs contain not only part-of-speech information but also attributes such as gender and case. In the case of Slovene there are 2,083 possible MSDs. P-Progol was used to learn morphosyntactic disambiguation rules from annotated data (consisting of 161,314 examples) produced by the MULTEXT-East project. P-Progol produced 1,148 rules taking 36 hours. Using simple grammatical background knowledge, e.g. looking for case disagreement, P-Progol induced 4,094 clauses in eight parallel runs. These rules have proved effective at detecting and explaining incorrect MSD annotations in an independent test set, but have not so far produced a tagger comparable to other existing taggers in terms of accuracy.