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.",
   }

Refereed Workshop Proceedings

1. Saurabh Kumar, Dhruvkumar Babubhai Kakadiya, and Sanasam Ranbir Singh. (2025, January). Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliteration - Roman to Devanagari using LLaMa. In Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, pages 129-134,Abu Dhabi. Association for Computational Linguistics.
   
   
2. Shifali Agrahari, Subhashi Jayant, Saurabh Kumar,and Sanasam Ranbir Singh. (2025, January). EssayDetect at GenAI Detection Task 2: Guardians of Academic Integrity: Multilingual Detection of AI-Generated Essays . In Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect), pages 299-306, Abu Dhabi, UAE. International Conference on Computational Linguistics.

   @inproceedings{agrahari-etal-2025-essaydetect,
             title = "{E}ssay{D}etect at {G}en{AI} Detection Task 2: Guardians of Academic Integrity: Multilingual Detection of {AI}-Generated Essays",
             author = "Agrahari, Shifali  and
               Jayant, Subhashi  and
               Kumar, Saurabh  and
               Ranbir Singh, Sanasam",
             editor = "Alam, Firoj  and
               Nakov, Preslav  and
               Habash, Nizar  and
               Gurevych, Iryna  and
               Chowdhury, Shammur  and
               Shelmanov, Artem  and
               Wang, Yuxia  and
               Artemova, Ekaterina  and
               Kutlu, Mucahid  and
               Mikros, George",
             booktitle = "Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)",
             month = jan,
             year = "2025",
             address = "Abu Dhabi, UAE",
             publisher = "International Conference on Computational Linguistics",
             url = "https://aclanthology.org/2025.genaidetect-1.33/",
             pages = "299--306",
             abstract = "Detecting AI-generated text in the field of academia is becoming very prominent. This paper presents a solution for Task 2: AI vs. Hu- man {--} Academic Essay Authenticity Challenge in the COLING 2025 DAIGenC Workshop 1. The rise of Large Language models (LLMs) like ChatGPT has posed significant challenges to academic integrity, particularly in detecting AI-generated essays. To address this, we pro- pose a fusion model that combines pre-trained language model embeddings with stylometric and linguistic features. Our approach, tested on both English and Arabic, utilizes adaptive training and attention mechanisms to enhance F1 scores, address class imbalance, and capture linguistic nuances across languages. This work advances multilingual solutions for detecting AI-generated text in academia."
         }