Shreya Agarwal is a Teaching Assistant in the Computer Science Department at IIIT-Surat. She has a strong academic background,
with a B.Tech and a Master's in Science, in Computer Science, from Dr. APJ Abdul Kalam Technical University and JK Institute of Applied Physics and
Technology, respectively. Her research interests lie in Natural Language Processing (NLP), and her recently submitted P.hD. thesis focuses on
developing anaphora resolution systems for Indian languages. Shreya is an active member of the academic community, having participated in
generative AI workshops with IIT-Ghandhinagar (organised by the ACM and sponsored by Microsoft) and advanced NLP courses at IIIT-Hyderabad.
In addition to her core research, she has also studied and presented on the topic of bias in automated systems against people with disabilities.
Her work was featured at a two-day national seminar, "The Past, Present and Future of Disability-Inclusive India: Celebrating 75 Years of India’s
Indepe
EDUCATION
B.Tech CSE, M.Sc CSE, PhD in NLP
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Information Systems, Computer Science Applications, Computational Theory and Mathematics
Unsupervised hindi word sense disambiguation using graph-based centrality measures Prajna Jha, Shreya Agarwal, Ali Abbas, Satyendr Singh, Tanveer Jahan Siddiqui Iaes International Journal of Artificial Intelligence, 2024 The task of word sense disambiguation (WSD) plays a key role in multiple applications of natural language processing. In this paper, we propose a novel unsupervised method for targeted Hindi WSD task. First, we create a weighted graph where the nodes correspond to various synsets of the target word and the neighboring context words. The edges in the graph represent the semantic relations between these synsets in the Hindi WordNet hierarchy. A path-based similarity measure, namely Leacock-Chodorow similarity measure, is used to assign weights to edges. An unsupervised weighted graph-based centrality algorithm is used to identify the correct sense of a target word in a given context. The performance of the proposed algorithm is measured on 20 ambiguous Hindi nouns using four different graph-based centrality measures. We observed a maximum accuracy of 66.92% using PageRank centrality measure which is significantly better than earlier reported graph-based Hindi WSD algorithmsevaluated on the same dataset.
Comparative Analysis of Path-based Similarity Measures for Word Sense Disambiguation Prajna Jha, Shreya Agarwal, Ali Abbas, Tanveer Siddiqui 2023 3rd International Conference on Artificial Intelligence and Signal Processing Aisp 2023, 2023 In this paper, we have implemented a novel unsupervised graph-based algorithm for Hindi Word Sense Disambiguation. For disambiguation, we perform a random walk on graph created for each instance. The nodes in the graph are various senses of the words appearing in the context of ambiguous word. The edge weights are assigned using semantic similarity between pair of nodes. We compare two path-based similarity measures. The experimental investigations suggest that a Leacock-Chodrow similarity measure performs better than Shortest path measure. We observed an accuracy of 72.09% averaged over all the instances of five Polysemous nouns.
RECENT SCHOLAR PUBLICATIONS
SAHA: Samvad AI for Healthcare Assistance A Kumar, RK Nayak, J Naik, R Kumar, D Bhatia, S Agarwal NLP-AI4Health, 80-85 , 2025 2025 Citations: 1
Harnessing AI for Health and Knowledge: An Investigation into Machine and Deep Learning Models for Medical and Textual Data A Abbas, S Agarwal, M Jaiswal, P Jha, TJ Siddiqui SN Computer Science 6 (6), 696 , 2025 2025 Citations: 1
A Novel Hybrid Method for Pronominal Anaphora Resolution in Hindi Text S Agarwal, P Jha, T Siddiqui Journal of Information Systems Engineering and Management 10 (42) , 2025 2025
A Hybrid Stacked Ensemble Model for Resolving Pronominal Anaphoric Ambiguity in Hindi Discourse S Agarwal, P Jha, A Abbas, TJ Siddiqui SN Computer Science 6 (3), 248 , 2025 2025
Unsupervised hindi word sense disambiguation using graph based centrality measures TJS Prajna Jha, Shreya Agarwal, Ali Abbas, Satyendr Singh IAES International Journal of Artificial Intelligence (IJ-AI) 14 (4), 4957-4964 , 2024 2024
Enhanced multi-class newsgroup document classification using N-gram approach A Abbas, M Jaiswal, S Agarwal, P Jha, TJ Siddiqui Migration Lett. 21 (6), 262-277 , 2024 2024 Citations: 2
A Novel Unsupervısed Graph-Based Algorıthm for Hindi Word Sense Disambiguation P Jha, S Agarwal, A Abbas, TJ Siddiqui SN Computer Science 4 (5), 675 , 2023 2023 Citations: 10
Performance Based Comparative Analysis of Naïve Bayes Variants for Text Classification A Abbas, M Jaiswal, S Agarwal, P Jha, TJ Siddiqui International Conference on Data Science and Communication, 295-310 , 2023 2023 Citations: 2
Comparative Analysis of Path-based Similarity Measures for Word Sense Disambiguation P Jha, S Agarwal, A Abbas, T Siddiqui 2023 3rd International conference on Artificial Intelligence and Signal … , 2023 2023 Citations: 4
Information Extraction for Design of a Multi-feature Hybrid Approach for Pronominal Anaphora Resolution in a Low Resource Language S Agarwal, P Jha, A Abbas, TJ Siddiqui International Conference on Advanced Computing, Machine Learning, Robotics … , 2023 2023 Citations: 1
A Hybrid Framework for Implementing Modified K-Means Clustering Algorithm for Hindi Word Sense Disambiguation P Jha, S Agarwal, A Abbas, TJ Siddiqui International Conference on Advanced Computing, Machine Learning, Robotics … , 2023 2023
Pronominal Anaphora Resolution in Hindi Discourse SA T J Siddiqui International Conference on Advancement in Electrical & Electronics … , 2022 2022
MOST CITED SCHOLAR PUBLICATIONS
A Novel Unsupervısed Graph-Based Algorıthm for Hindi Word Sense Disambiguation P Jha, S Agarwal, A Abbas, TJ Siddiqui SN Computer Science 4 (5), 675 , 2023 2023 Citations: 10
Comparative Analysis of Path-based Similarity Measures for Word Sense Disambiguation P Jha, S Agarwal, A Abbas, T Siddiqui 2023 3rd International conference on Artificial Intelligence and Signal … , 2023 2023 Citations: 4
Enhanced multi-class newsgroup document classification using N-gram approach A Abbas, M Jaiswal, S Agarwal, P Jha, TJ Siddiqui Migration Lett. 21 (6), 262-277 , 2024 2024 Citations: 2
Performance Based Comparative Analysis of Naïve Bayes Variants for Text Classification A Abbas, M Jaiswal, S Agarwal, P Jha, TJ Siddiqui International Conference on Data Science and Communication, 295-310 , 2023 2023 Citations: 2
SAHA: Samvad AI for Healthcare Assistance A Kumar, RK Nayak, J Naik, R Kumar, D Bhatia, S Agarwal NLP-AI4Health, 80-85 , 2025 2025 Citations: 1
Harnessing AI for Health and Knowledge: An Investigation into Machine and Deep Learning Models for Medical and Textual Data A Abbas, S Agarwal, M Jaiswal, P Jha, TJ Siddiqui SN Computer Science 6 (6), 696 , 2025 2025 Citations: 1
Information Extraction for Design of a Multi-feature Hybrid Approach for Pronominal Anaphora Resolution in a Low Resource Language S Agarwal, P Jha, A Abbas, TJ Siddiqui International Conference on Advanced Computing, Machine Learning, Robotics … , 2023 2023 Citations: 1
A Novel Hybrid Method for Pronominal Anaphora Resolution in Hindi Text S Agarwal, P Jha, T Siddiqui Journal of Information Systems Engineering and Management 10 (42) , 2025 2025
A Hybrid Stacked Ensemble Model for Resolving Pronominal Anaphoric Ambiguity in Hindi Discourse S Agarwal, P Jha, A Abbas, TJ Siddiqui SN Computer Science 6 (3), 248 , 2025 2025
Unsupervised hindi word sense disambiguation using graph based centrality measures TJS Prajna Jha, Shreya Agarwal, Ali Abbas, Satyendr Singh IAES International Journal of Artificial Intelligence (IJ-AI) 14 (4), 4957-4964 , 2024 2024
A Hybrid Framework for Implementing Modified K-Means Clustering Algorithm for Hindi Word Sense Disambiguation P Jha, S Agarwal, A Abbas, TJ Siddiqui International Conference on Advanced Computing, Machine Learning, Robotics … , 2023 2023
Pronominal Anaphora Resolution in Hindi Discourse SA T J Siddiqui International Conference on Advancement in Electrical & Electronics … , 2022 2022