Trapit Bansal

1.2k total citations
9 papers, 218 citations indexed

About

Trapit Bansal is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Trapit Bansal has authored 9 papers receiving a total of 218 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 1 paper in Molecular Biology. Recurrent topics in Trapit Bansal's work include Topic Modeling (8 papers), Natural Language Processing Techniques (3 papers) and Multimodal Machine Learning Applications (2 papers). Trapit Bansal is often cited by papers focused on Topic Modeling (8 papers), Natural Language Processing Techniques (3 papers) and Multimodal Machine Learning Applications (2 papers). Trapit Bansal collaborates with scholars based in United States and India. Trapit Bansal's co-authors include Andrew McCallum, Chiranjib Bhattacharyya, Sujith Ravi, Da-Cheng Juan, Patrick Verga, Nathan Greenberg, Ravindran Kannan, Tsendsuren Munkhdalai, Tong Wang and David Irwin and has published in prestigious journals such as arXiv (Cornell University), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Trapit Bansal

9 papers receiving 206 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Trapit Bansal United States	6	193	73	29	25	16	9	218
Minh C. Phan Singapore	7	191 1.0×	42 0.6×	33 1.1×	17 0.7×	12 0.8×	9	204
Leonhard Hennig Germany	10	250 1.3×	61 0.8×	11 0.4×	27 1.1×	7 0.4×	33	291
Francesco Piccinno Italy	9	200 1.0×	39 0.5×	30 1.0×	47 1.9×	6 0.4×	16	239
Xisen Jin United States	5	400 2.1×	48 0.7×	50 1.7×	89 3.6×	13 0.8×	7	427
Mehdi Allahyari United States	9	100 0.5×	47 0.6×	26 0.9×	8 0.3×	10 0.6×	12	135
Lianzhe Huang China	5	288 1.5×	41 0.6×	10 0.3×	27 1.1×	12 0.8×	5	319
Francesco Corcoglioniti Italy	8	141 0.7×	41 0.6×	23 0.8×	15 0.6×	8 0.5×	28	174
Shuofei Qiao China	7	149 0.8×	27 0.4×	21 0.7×	25 1.0×	5 0.3×	12	200
Karl Pichotta United States	4	207 1.1×	32 0.4×	19 0.7×	27 1.1×	7 0.4×	8	221
Shangwen Lv China	7	285 1.5×	74 1.0×	17 0.6×	55 2.2×	8 0.5×	11	309

Countries citing papers authored by Trapit Bansal

Since Specialization

Citations

This map shows the geographic impact of Trapit Bansal'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 Trapit Bansal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Trapit Bansal more than expected).

Fields of papers citing papers by Trapit Bansal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Trapit Bansal. 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 Trapit Bansal. The network helps show where Trapit Bansal may publish in the future.

Co-authorship network of co-authors of Trapit Bansal

This figure shows the co-authorship network connecting the top 25 collaborators of Trapit Bansal. A scholar is included among the top collaborators of Trapit Bansal 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 Trapit Bansal. Trapit Bansal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Bansal, Trapit, et al.. (2022). A moment in the sun. 251–262. 2 indexed citations

2.

Bansal, Trapit, et al.. (2021). Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 5812–5824. 15 indexed citations

3.

Bansal, Trapit, et al.. (2021). Simultaneously Self-Attending to Text and Entities for Knowledge-Informed Text Representations. 241–247. 1 indexed citations

4.

Bansal, Trapit, Da-Cheng Juan, Sujith Ravi, & Andrew McCallum. (2019). A2N: Attending to Neighbors for Knowledge Graph Inference. 4387–4392. 89 indexed citations

5.

Greenberg, Nathan, Trapit Bansal, Patrick Verga, & Andrew McCallum. (2018). Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets. 2824–2829. 37 indexed citations

6.

Bansal, Trapit, et al.. (2015). Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles. 195–202. 54 indexed citations

7.

Bansal, Trapit, et al.. (2015). Relating Romanized Comments to News Articles by Inferring Multi-Glyphic Topical Correspondence. Proceedings of the AAAI Conference on Artificial Intelligence. 29(1). 1 indexed citations

8.

Bansal, Trapit, et al.. (2014). Going beyond Corr-LDA for detecting specific comments on news & blogs. 483–492. 7 indexed citations

9.

Bansal, Trapit, Chiranjib Bhattacharyya, & Ravindran Kannan. (2014). A provable SVD-based algorithm for learning topics in dominant admixture corpus. arXiv (Cornell University). 27. 1997–2005. 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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