Bayesian inductive logic programming
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Learning language in logic
A probabilistic account of logical metonymy
Computational Linguistics
The Journal of Machine Learning Research
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
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Numeric approaches to the corpus-based acquisition of lexical semantic relations offer robust and portable techniques, but poor explanations of their results. On the other hand, symbolic machine learning approaches can infer patterns of a target relation from examples of elements that verify this relation; the produced patterns are efficient and expressive, but such techniques are often supervised, i.e. require to be (manually) fed by examples. This paper presents two original algorithms to combine one technique from each of these approaches, and keep advantages of both (meaningful patterns, efficient extraction, portability). Moreover the extraction results of these two semi-supervised hybrid systems, when applied in an illustrative purpose to the acquisition of semantic noun-verb relations defined in the Generative Lexicon framework (Pustejovsky, 1995), rival those of supervised methods.