Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A bootstrapping method for learning semantic lexicons using extraction pattern contexts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
An effective two-stage model for exploiting non-local dependencies in named entity recognition
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
A context pattern induction method for named entity extraction
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
A local tree alignment-based soft pattern matching approach for information extraction
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
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Since whether or not a word is a name is determined mostly by the context of the word, the context pattern induction plays an important role in name entity recognition (NER). We present a NER method based on the context pattern induction. It induces high-precision context patterns in an unsupervised way starting with some entity seeds. Then it uses directly the matched context patterns, instead of extracted entities by inducted patterns, as the features of a CRF-based NER model. The experiments show that the proposed method improves the performance of the high quality named entity recognizer, and achieves higher accuracy and recall rate.