The paper reports about the development of a Named Entity Recognition (NER) system in Bengali using a tagged Bengali news corpus and the subsequent transliteration of the recognized Bengali Named Entities (NEs) into English. Three different models of the NER have been developed. A semi-supervised learning method has been adopted to develop the first two models, one without linguistic features (Model A) and the other with linguistic features (Model B). The third one (Model C) is based on statistical Hidden Markov Model. A modified joint-source channel model has been used along with a number of alternatives to generate the English transliterations of Bengali NEs and vice-versa. The transliteration models learn the mappings from the bilingual training sets optionally guided by linguistic knowledge in the form of conjuncts and diphthongs in Bengali and their representations in English. The NER system has demonstrated the highest average Recall, Precision and F-Score values of 89.62%, 78.67% and 83.79% respectively in Model C. Evaluation of the proposed transliteration models demonstrated that the modified joint source-channel model performs best in terms of evaluation metrics for person and location names for both Bengali to English (B2E) transliteration and English to Bengali transliteration (E2B). The use of the linguistic knowledge during training of the transliteration models improves performance.
2019. Firefly Algorithm Based Multilingual Named Entity Recognition for Indian Languages. In Advanced Informatics for Computing Research [Communications in Computer and Information Science, 955], ► pp. 540 ff.
Das Dawn, Debapratim, Abhinandan Khan, Soharab Hossain Shaikh & Rajat Kumar Pal
2023. A dictionary based model for bengali document classification. Applied Intelligence 53:11 ► pp. 14023 ff.
Ekbal, Asif & Sivaji Bandyopadhyay
2009. 2009 Seventh International Conference on Advances in Pattern Recognition, ► pp. 259 ff.
Ekbal, Asif & Sivaji Bandyopadhyay
2010. Named Entity Recognition in Bengali . Northern European Journal of Language Technology 1 ► pp. 26 ff.
Ekbal, Asif & Sriparna Saha
2010. Classifier Ensemble Selection Using Genetic Algorithm for Named Entity Recognition. Research on Language and Computation 8:1 ► pp. 73 ff.
Ekbal, Asif & Sriparna Saha
2011. A multiobjective simulated annealing approach for classifier ensemble: Named entity recognition in Indian languages as case studies. Expert Systems with Applications 38:12 ► pp. 14760 ff.
Ekbal, Asif & Sriparna Saha
2012. Multiobjective optimization for classifier ensemble and feature selection: an application to named entity recognition. International Journal on Document Analysis and Recognition (IJDAR) 15:2 ► pp. 143 ff.
Ekbal, Asif & Sriparna Saha
2013. Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition. Soft Computing 17:1 ► pp. 1 ff.
Ekbal, Asif, Sriparna Saha & Utpal Kumar Sikdar
2016. On active annotation for named entity recognition. International Journal of Machine Learning and Cybernetics 7:4 ► pp. 623 ff.
Ekbal, Asif, Sriparna Saha & Dhirendra Singh
2012. Proceedings of the International Conference on Advances in Computing, Communications and Informatics, ► pp. 180 ff.
Ekbal, Asif, Sriparna Saha & Dhirendra Singh
2012. 2012 Third International Conference on Emerging Applications of Information Technology, ► pp. 331 ff.
Harish, B. S. & R. Kasturi Rangan
2020. A comprehensive survey on Indian regional language processing. SN Applied Sciences 2:7
2011. 2011 2nd National Conference on Emerging Trends and Applications in Computer Science, ► pp. 1 ff.
Prabhakar, Dinesh Kumar & Sukomal Pal
2018. Machine transliteration and transliterated text retrieval: a survey. Sādhanā 43:6
Rashid, Mohammad Rifat Ahmmad, Kazi Ferdous Hasan, Rakibul Hasan, Aritra Das, Mithila Sultana & Mahamudul Hasan
2024. A comprehensive dataset for sentiment and emotion classification from Bangladesh e-commerce reviews. Data in Brief 53 ► pp. 110052 ff.
Saha, Sriparna & Asif Ekbal
2013. Combining multiple classifiers using vote based classifier ensemble technique for named entity recognition. Data & Knowledge Engineering 85 ► pp. 15 ff.
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