An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Noun phrase recognition by system combination
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
ACM Transactions on Asian Language Information Processing (TALIP)
A bootstrapping approach to named entity classification using successive learners
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Text chunking by system combination
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Meta-learning orthographic and contextual models for language independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A stacked, voted, stacked model for named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
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This paper reports about the development of a Named Entity Recognition (NER) system in Bengali by combining the outputs of the two classifiers, namely Conditional Random Field (CRF) and Support Vector Machine (SVM). Lexical context patterns, which are generated from an unlabeled corpus of 10 million wordforms in an unsupervised way, have been used as the features of the classifiers in order to improve their performance. We have post-processed the models by considering the second best tag of CRF and class splitting technique of SVM in order to improve the performance. Finally, the classifiers are combined together into a final system using three weighted voting techniques. Experimental results show the effectiveness of the proposed approach with the overall average recall, precision, and f-score values of 91.33%, 88.19%, and 89.73%, respectively.