Prof.

Kamal Sarkar

Department : Department of Computer Science & Engineering

Designation : Professor

E-Mail: kamal.sarkar@jadavpuruniversity.in

Journal Papers Published

• A survey of hate speech detection in Indian languages, Nandi, A., Sarkar, K., Mallick, A., & De, A., (2024), Social Network Analysis and Mining, 14(1), 70.• Combining multiple pre-trained models for hate speech detection in Bengali, Marathi, and Hindi, Nandi, A., Sarkar, K., Mallick, A., & De, A., (2024). Multimedia Tools and Applications, 1-25.• Sentence Fusion using Deep Learning, Chowdhury, S. R., & Sarkar, K. , (2024), EAI Endorsed Transactions on Internet of Things, 10.• Bengali document retrieval using a language modeling approach enhanced by improved cluster-based smoothing, Chatterjee, S., & Sarkar, K. , (2023), Sādhanā, 48(4), 211.• Bengali Text Classification: A New multi-class Dataset and Performance Evaluation of Machine Learning and Deep Learning Models, Roy, A., Sarkar, K., & Mandal, C. K. , (2023). • A new method for extractive text summarization using neural networks, Chowdhury, S. R., & Sarkar, K. , (2023), SN Computer Science, 4(4), 384.• Exploiting semantic term relations in text summarization, Sarkar, K., & Dam, S. , (2022). International Journal of Information Retrieval Research (IJIRR), 12(1), 1-18.• Machine transliteration using SVM and HMM, Chatterjee, S., & Sarkar, K. , (2021), International Journal of Advanced Intelligence Paradigms, 19(1), 3-27.• Heterogeneous classifier ensemble for sentiment analysis of Bengali and Hindi tweets., Sarkar, K. ,(2020), Sādhanā, 45(1), 196.• Sentiment polarity detection in Bengali tweets using deep convolutional neural networks, Sarkar, K. ,(2019), Journal of Intelligent Systems, 28(3), 377-386.• Combining IR models for Bengali information retrieval, Chatterjee, S., & Sarkar, K. , (2018), International Journal of Information Retrieval Research (IJIRR), 8(3), 68-83.• Hindi named entity recognition using system combination, Sarkar, K., (2018), International Journal of Applied Pattern Recognition, 5(1), 11-39.• Recognition of spoken bengali numerals using MLP, SVM, RF based models with PCA based feature summarization, Gupta, A., & Sarkar, K., (2018), Int. Arab J. Inf. Technol., 15(2), 263-269.• A memory-based learning approach for named entity recognition in Hindi, Sarkar, K., & Shaw, S. K. ,(2017), Journal of Intelligent Systems, 26(2), 301-321.• A keyphrase-based approach to text summarization for english and bengali documents, Sarkar, K. ,(2014), International Journal of Technology Diffusion (IJTD), 5(2), 28-38.• Multilingual Summarization Approaches, Sarkar, Kamal., (2014), 10.4018/978-1-4666-5019-0.ch011• A hybrid approach to extract keyphrases from medical documents., Sarkar, K., (2013), arXiv preprint arXiv:1303.1441.• Using Decision Tree for Automatic Identification of Bengali Noun-Noun Compounds, Gayen, V., & Sarkar, K., (2013). • Machine learning based keyphrase extraction: comparing decision trees, naïve Bayes, and artificial neural networks, Sarkar, K., Nasipuri, M., & Ghose, S., (2012), Journal of Information Processing Systems, 8(4), 693-712.• Using machine learning for medical document summarization, Sarkar, K., Nasipuri, M., & Ghose, S. ,(2011), International Journal of Database Theory and Application, 4(1), 31-48.• A new approach to keyphrase extraction using neural networks, Sarkar, K., Nasipuri, M., & Ghose, S. ,(2010), arXiv preprint arXiv:1004.3274.• Syntactic trimming of extracted sentences for improving extractive multi-document summarization, Sarkar, K., (2010),Journal of Computing, 2(7), 177-184.• Using domain knowledge for text summarization in medical domain, Sarkar, K. ,(2009), International Journal of Recent Trends in Engineering, 1(1), 200.• Centroid-based summarization of multiple documents, Sarkar, K., (2009), TECHNIA–International Journal of Computing Science and Communication Technologies, 2.• Improving Keyphrase Extraction from Biomedical Documents Using Domain Specific Feature Set, Sarkar, K., (2009), International Journal of Recent Trends in Engineering, 2(3), 51.• Sentence clustering-based summarization of multiple text documents, Sarkar, K. ,(2009), TECHNIA–International Journal of Computing Science and Communication Technologies, 2(1), 325-335• A multilingual text summarization system for Indian languages, Sarkar, K., & Bandyopadhyay, S, (2005), Proceedings of Simple.• Automatic single document text summarization using key concepts in documents, Sarkar, K., (2013), Journal of information processing systems, 9(4), 602-620.• A study on Effect of Positional Information in Single Document Text Summarization, Dam,S., Sarkar, K., & Chowdhury, S. R., (2018), International Journal of Computing and Application (IJCA), Vol 16(1), pp 29-37, ISSN: 0973-5704.• Topic sentiment analysis for twitter data in indian languages using composite kernel svm and deep learning, Maity, S., & Sarkar, K. , (2022). ACM Transactions on Asian and Low-Resource Language Information Processing, 21(5), 1-35.

Publication in Conference Proceedings

• A Hybrid Query Expansion Method for Effective Bengali Information Retrieval, Chatterjee, S., Sarkar, K., & Patra, S. ,(2023, October), In International Conference on Frontiers in Computing and Systems (pp. 377-397). Singapore: Springer Nature Singapore.• Abstractive Multi-Document Summarization Using Sentence Fusion, Chowdhury, S. R., & Sarkar, K., (2023, August), In 2023 International Conference on Information Technology (ICIT) (pp. 734-741). IEEE.• Bengali Document Clustering: A Comparative Study of K-Means, K-Means++, Spectral K-Means, Roy, A., Sarkar, K., & Mandal, C. , (2022, December), In International Conference on Advanced Computing and Intelligent Engineering (pp. 27-39). Singapore: Springer Nature Singapore.• Bengali Document Retrieval Using Model Combination, Chatterjee, S., & Sarkar, K. ,(2022, December),In International Conference on Frontiers in Computing and Systems (pp. 91-101). Singapore: Springer Nature Singapore.• Unsupervised bengali text summarization using sentence embedding and spectral clustering, Roychowdhury, S., Sarkar, K., & Maji, A., (2022, December), In Proceedings of the 19th International Conference on Natural Language Processing (ICON) (pp. 337-346).• Predicting Word Importance Using a Support Vector Regression Model for Multi-document Text Summarization, Chatterjee, S., & Sarkar, K., (2022, September), In International Conference on Advances in Data-driven Computing and Intelligent Systems (pp. 83-97). Singapore: Springer Nature Singapore.• Bengali POS tagging using bi-LSTM with word embedding and character-level embedding, Bose, K., & Sarkar, K., (2022, June), In Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021 (pp. 561-570). Singapore: Springer Nature Singapore.• A Novel Sentence Scoring Method for Extractive Text Summarization, Sarkar, K., & Chowdhury, S. R. ,(2021), In Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2020 (pp. 169-179). Springer Singapore.• Rice leaf diseases classification using CNN with transfer learning, Ghosal, S., & Sarkar, K. ,(2020, February),In 2020 IEEE Calcutta Conference (Calcon) (pp. 230-236). IEEE.• Analyzing large news corpus using text mining techniques for recognizing high crime prone areas,Mukherjee, S., & Sarkar, K. ,(2020, February), In 2020 IEEE Calcutta Conference (CALCON) (pp. 444-450). IEEE.• Sentiment analysis of Bengali tweets using deep learning, Sarkar, K. ,(2020),In Computational Intelligence in Data Science: Third IFIP TC 12 International Conference, ICCIDS 2020, Chennai, India, February 20–22, 2020, Revised Selected Papers 3 (pp. 71-84). Springer International Publishing.• Irony detection in bengali tweets: A new dataset, experimentation and results, Ghosh, A., & Sarkar, K. ,(2020), In Computational Intelligence in Data Science: Third IFIP TC 12 International Conference, ICCIDS 2020, Chennai, India, February 20–22, 2020, Revised Selected Papers 3 (pp. 112-127). Springer International Publishing.• A stacked ensemble approach to bengali sentiment analysis, Sarkar, K. ,(2020), In Intelligent Human Computer Interaction: 11th International Conference, IHCI 2019, Allahabad, India, December 12–14, 2019, Proceedings 11 (pp. 102-111). Springer International Publishing.• Sentiment polarity detection in Bengali tweets using LSTM recurrent neural networks, Sarkar, K. ,(2019, February), In 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP) (pp. 1-6). IEEE.• Sentiment analysis for Indian code mixed social media texts, Sarkar, K. ,(2018), Ju_ks@ sail_codemixed-2017:arXiv preprint, NLP tool contest @ ICON2017, arXiv:1802.05737.• Automatic text summarization using intenal and extemal information, Sarkar, K. ,(2018, January), In 2018 fifth international conference on emerging applications of information technology (EAIT) (pp. 1-4). IEEE.• Using character n-gram features and multinomial naïve bayes for sentiment polarity detection in Bengali tweets, Sarkar, K., (2018, January), In 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT) (pp. 1-4). IEEE.• Learning to Detect Paraphrases in Indian Languages, Sarkar, Kamal, In Text Processing: FIRE 2016 International Workshop, Kolkata, India, December 7–10, 2016, Revised Selected Papers, pp. 153-165. Springer International Publishing, 2018.• An approach to generic Bengali text summarization using latent semantic analysis., Chowdhury, S. R., Sarkar, K., & Dam, S., (2017, December), In 2017 international conference on information technology (ICIT) (pp. 11-16). IEEE.• Sentiment polarity detection in bengali tweets using multinomial Naïve Bayes and support vector machines,Sarkar, K., & Bhowmick, M. , (2017, December), In 2017 IEEE Calcutta Conference (CALCON) (pp. 31-36). IEEE.• An empirical study of some selected ir models for Bengali monolingual information retrieval, Sarkar, K., & Gupta, A. , (2017), arXiv preprint arXiv:1706.03266.• Bengali-to-english forward and backward machine transliteration using support vector machines,Sarkar, K., & Chatterjee, S. ,(2017), In Computational Intelligence, Communications, and Business Analytics: First International Conference, CICBA 2017, Kolkata, India, March 24–25, 2017, Revised Selected Papers, Part II (pp. 552-566). Springer Singapore.• Consumer health information search, Sarkar, K., Das, D., Banerjee, I., Kumari, M., & Biswas, P. ,(2016),JU_KS_Group@ FIRE 2016, arXiv preprint arXiv:1612.08178.• Detecting paraphrases in Indian languages using multinomial logistic regression model, Sarkar, K. , (2016), KS_JU@ DPIL-FIRE2016: arXiv preprint arXiv:1612.08171.• A CRF based POS tagger for code-mixed Indian social media text, Sarkar, K., (2016), arXiv preprint arXiv:1612.07956.• A comparative study of gene selection methods for cancer classification using microarray data, Babu, M., & Sarkar, K. ,(2016, September), In 2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) (pp. 204-211). IEEE.• Part-of-speech tagging for code-mixed indian social media text, Sarkar, K. (2016), In icon 2015. arXiv preprint arXiv:1601.01195.• A hidden markov model based system for entity extraction from social media english text, Sarkar, K. ,(2015), at fire 2015. arXiv preprint arXiv:1512.03950.• Improving graph based multidocument text summarization using an enhanced sentence similarity measure, Sarkar, K., Saraf, K., & Ghosh, A, (2015, July), In 2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS) (pp. 359-365). IEEE.• A sentiment analysis system for Indian language tweets, Sarkar, K., & Chakraborty, S. , (2015), In Mining Intelligence and Knowledge Exploration: Third International Conference, MIKE 2015, Hyderabad, India, December 9-11, 2015, Proceedings 3 (pp. 694-702). Springer International Publishing.• An improved approach to bengali keyphrase extraction, Sarkar, K., (2014, December), In 2014 Fourth International Conference of Emerging Applications of Information Technology (pp. 283-288). IEEE• An HMM based named entity recognition system for indian languages, Gayen, V., & Sarkar, K., (2014), In the JU system at ICON 2013. arXiv preprint arXiv:1405.7397.• A machine learning approach for the identification of Bengali noun-noun compound multiword expressions, Gayen, V., & Sarkar, K. , (2014), arXiv preprint arXiv:1401.6567.• Bengali noun phrase chunking based on conditional random fields, Sarkar, K., & Gayen, V., (2014, January), In 2014 2nd International Conference on Business and Information Management (ICBIM) (pp. 148-153). IEEE.• A Memory Based POS Tagger for Bengali, Sarkar, K., & Ghosh, A. , (2013). • Automatic identification of Bengali noun-noun compounds using random forest, Gayen, V., & Sarkar, K. , (2013, June), In Proceedings of the 9th Workshop on Multiword Expressions (pp. 64-72).• A trigram HMM-based POS tagger for Indian languages, Sarkar, K., & Gayen, V. ,(2013), In Proceedings of the international conference on frontiers of intelligent computing: theory and applications (FICTA) (pp. 205-212). Springer Berlin Heidelberg.• A practical part-of-speech tagger for Bengali, Sarkar, K., & Gayen, V. , (2012, November), In 2012 Third International Conference on Emerging Applications of Information Technology (pp. 36-40). IEEE.• An approach to summarizing Bengali news documents, Sarkar, K. , (2012, August), In proceedings of the International Conference on Advances in Computing, Communications and Informatics (pp. 857-862).• Bengali text summarization by sentence extraction, Sarkar, K., (2012), arXiv preprint arXiv:1201.2240.• An n-gram based method for bengali keyphrase extraction, Sarkar, K. , (2011, March), In International Conference on Information Systems for Indian Languages (pp. 36-41). Berlin, Heidelberg: Springer Berlin Heidelberg.• Automatic keyphrase extraction from bengali documents: A preliminary study, Sarkar, K. , (2011, February), In 2011 Second International Conference on Emerging Applications of Information Technology (pp. 125-128). IEEE.• Improving multi-document text summarization performance using local and global trimming, Sarkar, K., (2009), In Proceedings of the First International Conference on Intelligent Human Computer Interaction: (IHCI 2009) January 20–23, 2009 Organized by the Indian Institute of Information Technology, Allahabad, India (pp. 272-282). Springer India.• Automatic keyphrase extraction from medical documents, Sarkar, K. , (2009), In Pattern Recognition and Machine Intelligence: Third International Conference, PReMI 2009 New Delhi, India, December 16-20, 2009 Proceedings 3 (pp. 273-278). Springer Berlin Heidelberg.• Syntactic sentence compression: Facilitating web browsing on mobile devices, Sarkar, K. , (2008, December), In 2008 International Conference on Information Technology (pp. 283-286). IEEE.• Generating headline summary from a document set, Sarkar, K., & Bandyopadhyay, S., (2005), In Computational Linguistics and Intelligent Text Processing: 6th International Conference, CICLing 2005, Mexico City, Mexico, February 13-19, 2005. Proceedings 6 (pp. 649-652). Springer Berlin Heidelberg.• Generating headline summary from a document set, Sarkar, K., & Bandyopadhyay, S. ,(2005), In Computational Linguistics and Intelligent Text Processing: 6th International Conference, CICLing 2005, Mexico City, Mexico, February 13-19, 2005. Proceedings 6 (pp. 649-652). Springer Berlin Heidelberg.• AN ARCHITECTURE OF MULTILINGUAL NEWS SUMMARIZATION SYSTEM FOR ENGLISH AND BANGLA, Sarkar, K., & Bandyopadhyay, S., (2005)• Improving Salience-based Multi-document Summarization performance Using a Hybrid Sentence Similarity Measure, Sarkar, K., Chowdhury, S. R., (2024), 4th International conference on NLP and Text Mining(NLTM 2024)

NBA Accreditation:UG-CSE,ETCE (6yrs) & UG-IT,IEE,PE (3yrs)

Prof.

Kamal Sarkar

Department : Department of Computer Science & Engineering

Designation : Professor

Journal Papers Published

Publication in Conference Proceedings

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Faculty

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NBA Accreditation:UG-CSE,ETCE (6yrs) & UG-IT,IEE,PE (3yrs)