Partha Talukdar

7.0k total citations · 1 hit paper
120 papers, 3.0k citations indexed

About

Partha Talukdar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Partha Talukdar has authored 120 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 84 papers in Artificial Intelligence, 17 papers in Computer Vision and Pattern Recognition and 11 papers in Information Systems. Recurrent topics in Partha Talukdar's work include Topic Modeling (51 papers), Natural Language Processing Techniques (43 papers) and Advanced Graph Neural Networks (16 papers). Partha Talukdar is often cited by papers focused on Topic Modeling (51 papers), Natural Language Processing Techniques (43 papers) and Advanced Graph Neural Networks (16 papers). Partha Talukdar collaborates with scholars based in United States, India and United Kingdom. Partha Talukdar's co-authors include Tom M. Mitchell, Fernando Pereira, Aditay Tripathi, Apoorv Saxena, Swayambhu Nath Ray, Brian Murphy, Shib Sankar Dasgupta, Amarnag Subramanya, Alona Fyshe and Matt Gardner and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Knowledge and Data Engineering and Proceedings of the VLDB Endowment.

In The Last Decade

Partha Talukdar

114 papers receiving 2.8k citations

Hit Papers

Improving Multi-hop Question Answering over Knowledge Gra... 2020 2026 2022 2024 2020 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
    2020 269 citations Partha Talukdar et al. profile →

Peers

Partha Talukdar
Maximilian Nickel United States
Danai Koutra United States
Andrew Kachites McCallum United States
Huanbo Luan China
Carl Yang United States
Prithviraj Sen United States
Max Chickering United States
Estevam Hruschka Brazil
Praveen Paritosh United States
Antoine Bordes Israel
Maximilian Nickel United States
Partha Talukdar
Citations per year, relative to Partha Talukdar Partha Talukdar (= 1×) peers Maximilian Nickel

Countries citing papers authored by Partha Talukdar

Since Specialization
Citations

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

Fields of papers citing papers by Partha Talukdar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Partha Talukdar

This figure shows the co-authorship network connecting the top 25 collaborators of Partha Talukdar. A scholar is included among the top collaborators of Partha Talukdar 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 Partha Talukdar. Partha Talukdar 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.
Singh, Harman Preet, et al.. (2024). IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages. 11047–11073. 3 indexed citations
2.
Kazemi, Mehran, et al.. (2023). TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs. 2049–2060. 5 indexed citations
3.
4.
Talukdar, Partha, Anthony J. Travis, Mahmud Hossain, et al.. (2021). Identification of genomic loci regulating grain iron content in aus rice under two irrigation management systems. Food and Energy Security. 11(1). e329–e329. 10 indexed citations
5.
Talukdar, Partha, et al.. (2019). Zero-shot Word Sense Disambiguation using Sense Definition Embeddings. 5670–5681. 64 indexed citations
6.
Norton, Gareth J., Anthony J. Travis, Partha Talukdar, et al.. (2019). Genetic loci regulating arsenic content in rice grains when grown flooded or under alternative wetting and drying irrigation. Rice. 12(1). 54–54. 31 indexed citations
7.
Yadati, Naganand, Madhav Nimishakavi, Prateek Yadav, et al.. (2019). HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. ePrints@IISc (Indian Institute of Science). 32. 1509–1520. 26 indexed citations
8.
Vashishth, Shikhar, et al.. (2018). Graph Convolutional Networks based Word Embeddings.. arXiv (Cornell University). 3 indexed citations
9.
Talukdar, Partha, et al.. (2018). Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention. arXiv (Cornell University). 7 indexed citations
10.
Yadati, Naganand, Madhav Nimishakavi, Prateek Yadav, Anand Louis, & Partha Talukdar. (2018). HyperGCN: Hypergraph Convolutional Networks for Semi-Supervised Classification.. arXiv (Cornell University). 12 indexed citations
11.
Talukdar, Partha, et al.. (2017). KGEval: Accuracy Estimation of Automatically Constructed Knowledge Graphs. 1741–1750. 11 indexed citations
12.
Talukdar, Partha, et al.. (2015). AskWorld: budget-sensitive query evaluation for knowledge-on-demand. International Conference on Artificial Intelligence. 837–843. 3 indexed citations
13.
Gardner, Matt, Partha Talukdar, & Tom M. Mitchell. (2015). Combining Vector Space Embeddings with Symbolic Logical Inference over Open-Domain Text.. National Conference on Artificial Intelligence. 6 indexed citations
14.
Gardner, Matt, Partha Talukdar, Bryan Kisiel, & Tom M. Mitchell. (2013). Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues. Figshare. 833–838. 58 indexed citations
15.
Kisiel, Bryan, Justin Betteridge, Matt Gardner, et al.. (2013). CMUML System for KBP 2013 Slot Filling.. Theory and applications of categories. 1 indexed citations
16.
Dhillon, Paramveer S., Partha Talukdar, & Koby Crammer. (2012). Metric Learning for Graph-Based Domain Adaptation. ScholarlyCommons (University of Pennsylvania). 255–264. 4 indexed citations
17.
Talukdar, Partha & Fernando Pereira. (2010). Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition. Meeting of the Association for Computational Linguistics. 1473–1481. 58 indexed citations
18.
Blitzer, John, et al.. (2008). Regularized Learning with Networks of Features. Neural Information Processing Systems. 21. 1401–1408. 30 indexed citations
19.
Dredze, Mark, et al.. (2007). Frustratingly Hard Domain Adaptation for Dependency Parsing. Empirical Methods in Natural Language Processing. 1051–1055. 57 indexed citations
20.
Bali, Kalika, et al.. (2004). Tools for the development of a Hindi speech synthesis system.. SSW. 109–114. 26 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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