Preprints

Refereed journal articles

1. Sujit Kumar, Saurabh Kumar, and Sanasam Ranbir Singh. (2024, April). A Headline-Centric Graph-Based Dual Context Matching Approach for Incongruent News Detection. In IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2024.3384698.
   
   

Refereed conference proceedings

1. Saurabh Kumar, Sanasam Ranbir Singh, and Sukumar Nandi. (2024, June). IndiSentiment140: Sentiment Analysis Dataset for Indian Languages with Emphasis on Low-Resource Languages using Machine Translation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7682–7691, Mexico City, Mexico. Association for Computational Linguistics.
   
   
2. Saurabh Kumar, Sanasam Ranbir Singh, and Sukumar Nandi. (2023, December). IndiSocialFT: Multilingual Word Representation for Indian languages in code-mixed environment . In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3866–3871, Singapore. Association for Computational Linguistics.
   BIB Poster PDF

   @inproceedings{kumar-etal-2023-indisocialft,
     title = "{I}ndi{S}ocial{FT}: Multilingual Word Representation for {I}ndian languages in code-mixed environment",
     author = "Kumar, Saurabh  and
       Sanasam, Ranbir  and
       Nandi, Sukumar",
     editor = "Bouamor, Houda  and
       Pino, Juan  and
       Bali, Kalika",
     booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
     month = dec,
     year = "2023",
     address = "Singapore",
     publisher = "Association for Computational Linguistics",
     url = "https://aclanthology.org/2023.findings-emnlp.252",
     doi = "10.18653/v1/2023.findings-emnlp.252",
     pages = "3866--3871",
     abstract = "The increasing number of Indian language users on the internet necessitates the development of Indian language technologies. In response to this demand, our paper presents a generalized representation vector for diverse text characteristics, including native scripts, transliterated text, multilingual, code-mixed, and social media-related attributes. We gather text from both social media and well-formed sources and utilize the FastText model to create the {``}IndiSocialFT{''} embedding. Through intrinsic and extrinsic evaluation methods, we compare IndiSocialFT with three popular pretrained embeddings trained over Indian languages. Our findings show that the proposed embedding surpasses the baselines in most cases and languages, demonstrating its suitability for various NLP applications.",
   }