Mitesh M. Khapra

3.5k total citations
86 papers, 1.5k citations indexed

About

Mitesh M. Khapra is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Mitesh M. Khapra has authored 86 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 78 papers in Artificial Intelligence, 19 papers in Computer Vision and Pattern Recognition and 4 papers in Signal Processing. Recurrent topics in Mitesh M. Khapra's work include Natural Language Processing Techniques (59 papers), Topic Modeling (56 papers) and Speech and dialogue systems (15 papers). Mitesh M. Khapra is often cited by papers focused on Natural Language Processing Techniques (59 papers), Topic Modeling (56 papers) and Speech and dialogue systems (15 papers). Mitesh M. Khapra collaborates with scholars based in India, United States and United Kingdom. Mitesh M. Khapra's co-authors include Pratyush Kumar, Anoop Kunchukuttan, Pushpak Bhattacharyya, Balaraman Ravindran, Sarath Chandar, Divyanshu Kakwani, Satish Golla, Shweta Bhardwaj, Sumanth Doddapaneni and Amrita Saha and has published in prestigious journals such as ACM Computing Surveys, Neural Computation and Language Resources and Evaluation.

In The Last Decade

Mitesh M. Khapra

81 papers receiving 1.4k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mitesh M. Khapra India 19 1.2k 442 141 89 61 86 1.5k
Kemal Oflazer Türkiye 25 1.7k 1.4× 260 0.6× 115 0.8× 26 0.3× 119 2.0× 110 2.0k
Faisal Ahmed Bangladesh 10 386 0.3× 367 0.8× 73 0.5× 45 0.5× 31 0.5× 32 739
Marjan Ghazvininejad United States 15 1.1k 0.9× 421 1.0× 78 0.6× 23 0.3× 51 0.8× 32 1.3k
Marta R. Costa‐jussà Spain 18 1.6k 1.3× 347 0.8× 112 0.8× 24 0.3× 35 0.6× 149 1.7k
Ting Liu China 21 1.2k 1.0× 180 0.4× 179 1.3× 34 0.4× 31 0.5× 74 1.4k
Tunga Güngör Türkiye 17 619 0.5× 183 0.4× 196 1.4× 20 0.2× 62 1.0× 75 839
Melvin Johnson United States 9 1.4k 1.2× 582 1.3× 71 0.5× 18 0.2× 67 1.1× 15 1.6k
Abdelmajid Ben Hamadou Tunisia 13 481 0.4× 157 0.4× 115 0.8× 28 0.3× 59 1.0× 95 683
Kevin Duh United States 24 2.1k 1.7× 490 1.1× 216 1.5× 25 0.3× 136 2.2× 148 2.4k
Stanley F. Chen United States 15 2.2k 1.8× 252 0.6× 236 1.7× 43 0.5× 295 4.8× 22 2.5k

Countries citing papers authored by Mitesh M. Khapra

Since Specialization
Citations

This map shows the geographic impact of Mitesh M. Khapra's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mitesh M. Khapra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mitesh M. Khapra more than expected).

Fields of papers citing papers by Mitesh M. Khapra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mitesh M. Khapra. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mitesh M. Khapra. The network helps show where Mitesh M. Khapra may publish in the future.

Co-authorship network of co-authors of Mitesh M. Khapra

This figure shows the co-authorship network connecting the top 25 collaborators of Mitesh M. Khapra. A scholar is included among the top collaborators of Mitesh M. Khapra based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mitesh M. Khapra. Mitesh M. Khapra is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Doddapaneni, Sumanth, et al.. (2025). A Primer on Pretrained Multilingual Language Models. ACM Computing Surveys. 57(9). 1–39. 1 indexed citations
3.
Sankar, Ananth, Sumanth Doddapaneni, Anoop Kunchukuttan, et al.. (2024). IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages. 15831–15879. 5 indexed citations
4.
Goyal, Shreya, Sumanth Doddapaneni, Mitesh M. Khapra, & Balaraman Ravindran. (2023). A Survey of Adversarial Defenses and Robustness in NLP. ACM Computing Surveys. 55(14s). 1–39. 63 indexed citations
5.
Khapra, Mitesh M., et al.. (2021). A Joint Training Framework for Open-World Knowledge Graph Embeddings. 3 indexed citations
6.
Khapra, Mitesh M., et al.. (2021). Perturbation CheckLists for Evaluating NLG Evaluation Metrics. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 7219–7234. 27 indexed citations
8.
Khapra, Mitesh M., et al.. (2019). Data Interpretation over Plots.. arXiv (Cornell University). 1 indexed citations
9.
Khapra, Mitesh M., et al.. (2014). When Transliteration Met Crowdsourcing : An Empirical Study of Transliteration via Crowdsourcing using Efficient, Non-redundant and Fair Quality Control. Language Resources and Evaluation. 196–202. 12 indexed citations
10.
Visweswariah, Karthik, et al.. (2013). Cut the noise: Mutually reinforcing reordering and alignments for improved machine translation. Meeting of the Association for Computational Linguistics. 1275–1284. 2 indexed citations
11.
Khapra, Mitesh M., et al.. (2013). Improving reordering performance using higher order and structural features. North American Chapter of the Association for Computational Linguistics. 315–324. 5 indexed citations
12.
Khapra, Mitesh M., et al.. (2011). Together We Can: Bilingual Bootstrapping for WSD. Meeting of the Association for Computational Linguistics. 1. 561–569. 10 indexed citations
13.
Khapra, Mitesh M., et al.. (2011). It Takes Two to Tango: A Bilingual Unsupervised Approach for Estimating Sense Distributions using Expectation Maximization. International Joint Conference on Natural Language Processing. 695–704. 4 indexed citations
14.
Khapra, Mitesh M., et al.. (2010). CFILT: Resource Conscious Approaches for All-Words Domain Specific WSD. Meeting of the Association for Computational Linguistics. 421–426. 4 indexed citations
15.
Khapra, Mitesh M., et al.. (2010). All Words Domain Adapted WSD: Finding a Middle Ground between Supervision and Unsupervision. Meeting of the Association for Computational Linguistics. 1532–1541. 17 indexed citations
16.
Kumaran, A., Mitesh M. Khapra, & Haizhou Li. (2010). Report of NEWS 2010 Transliteration Mining Shared Task. Meeting of the Association for Computational Linguistics. 21–28. 23 indexed citations
17.
Kumaran, A., Mitesh M. Khapra, & Haizhou Li. (2010). Whitepaper of NEWS 2010 Shared Task on Transliteration Mining. Meeting of the Association for Computational Linguistics. 29–38. 13 indexed citations
18.
Khapra, Mitesh M., et al.. (2010). Verbs are where all the action lies: Experiences of Shallow Parsing of a Morphologically Rich Language. International Conference on Computational Linguistics. 347–355. 9 indexed citations
19.
Khapra, Mitesh M., Raghavendra Udupa, A. Kumaran, & Pushpak Bhattacharyya. (2010). PR + RQ ALMOST EQUAL TO PQ: Transliteration Mining Using Bridge Language.. National Conference on Artificial Intelligence. 2 indexed citations
20.
Khapra, Mitesh M., A. Kumaran, & Pushpak Bhattacharyya. (2010). Everybody loves a rich cousin: An empirical study of transliteration through bridge languages. North American Chapter of the Association for Computational Linguistics. 420–428. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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