Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
LearningPinocchio: adaptive information extraction for real world applications
Natural Language Engineering
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Journal of the American Society for Information Science and Technology - Bioinformatics
Adaptive information extraction from text by rule induction and generalisation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Understanding queries in a search database system
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Bootstrapping ontology evolution with multimedia information extraction
Knowledge-driven multimedia information extraction and ontology evolution
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In this paper we propose a novel relation extraction method, based on grammatical inference. Following a semisupervised learning approach, the text that connects named entities in an annotated corpus is used to infer a context free grammar. The grammar learning algorithm is able to infer grammars from positive examples only, controlling overgeneralisation through minimum description length. Evaluation results show that the proposed approach performs comparable to the state of the art, while exhibiting a bias towards precision, which is a sign of conservative generalisation.