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• W2174665215 abstract "This paper reports about a multi-engine approach for the development of a NER system in Bengali by combining the classifiers such as Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM) with the help of weighted voting approach. The training set consists of approximately 272K wordforms, out of which 150K wordforms have been manually annotated with the four major named entity (NE) tags such as Person, Location, Organization and Miscellaneous tags. An appropriate tag conversion routine has been defined in order to convert the 122K wordforms of the IJCNLP-08 NER shared task, into the desired forms. The classifiers make use of the different contextual information of the words along with the variety of features that are helpful in predicting the various NE classes. Lexical context patterns, which are generated from an unlabeled corpus of 3 million wordforms in a semi-automatic way, have been used as the features of the classifiers in order to improve their performance. In addition, we have developed a number of techniques to post-process the output of each of the classifiers in order to reduce the errors and to improve the performance. Finally, we have applied weighted voting approach to combine the systems. Results show the effectiveness of the proposed approach with the overall average recall, precision, and f-score values of 93.98%, 90.63%, and 92.28%, respectively, which shows an improvement of 14.92% in f-score over the best performing baseline SVM based system and an improvement of 18.36% in f-score over the least performing baseline ME based system. The proposed system also outperforms the other existing Bengali NER system." @default.
• W2174665215 created "2016-06-24" @default.
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• W2174665215 date "2008-11-01" @default.
• W2174665215 modified "2023-09-26" @default.
• W2174665215 title "Multi-Engine Approach for Named Entity Recognition in Bengali" @default.
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