Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
A maximum entropy-based word sense disambiguation system
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Named Entity Extraction using AdaBoost
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Introduction to the CoNLL-2002 shared task: language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition using hundreds of thousands of features
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Corpus-based semantic role approach in information retrieval
Data & Knowledge Engineering
Bootstrapping named entity recognition with automatically generated gazetteer lists
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Self-training and co-training applied to spanish named entity recognition
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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The increasing flow of digital information requires the extraction, filtering and classification of pertinent information from large volumes of texts. An important preprocessing tool of these tasks consists of name entities recognition, which corresponds to a Name Entity Recognition (NER) task. In this paper we propose a completely automatic NER which involves identification of proper names in texts, and classification into a set of predefined categories of interest as Person names, Organizations (companies, government organizations, committees, etc.) and Locations (cities, countries, rivers, etc). We examined the differences in language models learned by different data-driven systems performing the same NLP tasks and how they can be exploited to yield a higher accuracy than the best individual system. Three NE classifiers (Hidden Markov Models, Maximum Entropy and Memory-based learner) are trained on the same corpus data and after comparison their outputs are combined using voting strategy. Results are encouraging since 98.5% accuracy for recognition and 84.94% accuracy for classification of NE for Spanish language were achieved.