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.
Archives of Biochemistry and Biophysics
2009750 citationsPushpak Bhattacharyya et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
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Countries citing papers authored by Pushpak Bhattacharyya
Since
Specialization
Citations
This map shows the geographic impact of Pushpak Bhattacharyya'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 Pushpak Bhattacharyya with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pushpak Bhattacharyya more than expected).
Fields of papers citing papers by Pushpak Bhattacharyya
This network shows the impact of papers produced by Pushpak Bhattacharyya. 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 Pushpak Bhattacharyya. The network helps show where Pushpak Bhattacharyya may publish in the future.
Co-authorship network of co-authors of Pushpak Bhattacharyya
This figure shows the co-authorship network connecting the top 25 collaborators of Pushpak Bhattacharyya.
A scholar is included among the top collaborators of Pushpak Bhattacharyya 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 Pushpak Bhattacharyya. Pushpak Bhattacharyya is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Firdaus, Mauajama, Asif Ekbal, & Pushpak Bhattacharyya. (2020). Incorporating Politeness across Languages in Customer Care Responses: Towards building a Multi-lingual Empathetic Dialogue Agent.. Language Resources and Evaluation. 4172–4182.8 indexed citations
3.
Bhattacharyya, Pushpak, et al.. (2019). IIT Bombay at HASOC 2019: Supervised Hate Speech and Offensive Content Detection in Indo-European Languages.. 352–358.3 indexed citations
4.
Gupta, Deepak, et al.. (2018). MMQA: A Multi-domain Multi-lingual Question-Answering Framework for English and Hindi. Language Resources and Evaluation.11 indexed citations
5.
Sen, Sukanta, et al.. (2016). Can SMT and RBMT Improve each other's Performance?- An Experiment with English-Hindi Translation.. 10–19.5 indexed citations
6.
Kumar, Ayush, et al.. (2016). A Hybrid Deep Learning Architecture for Sentiment Analysis. International Conference on Computational Linguistics. 482–493.65 indexed citations
7.
Bhattacharyya, Pushpak, et al.. (2015). Detection of Multiword Expressions for Hindi Language using Word Embeddings and WordNet-based Features.. 295–302.4 indexed citations
Chatterjee, Rajen, Anoop Kunchukuttan, & Pushpak Bhattacharyya. (2014). Supertag Based Pre-ordering in Machine Translation.. 30–38.1 indexed citations
10.
Joshi, Aditya, et al.. (2013). Making Headlines in Hindi: Automatic English to Hindi News Headline Translation. International Joint Conference on Natural Language Processing. 21–24.2 indexed citations
11.
Bhattacharyya, Pushpak, et al.. (2013). IITB-Sentiment-Analysts: Participation in Sentiment Analysis in Twitter SemEval 2013 Task. Joint Conference on Lexical and Computational Semantics. 495–500.9 indexed citations
12.
Popat, Kashyap, et al.. (2013). The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis. Meeting of the Association for Computational Linguistics. 412–422.18 indexed citations
13.
Joshi, Aditya, et al.. (2012). Cost and Benefit of Using WordNet Senses for Sentiment Analysis. Language Resources and Evaluation. 3090–3097.2 indexed citations
14.
Mukherjee, Subhabrata & Pushpak Bhattacharyya. (2012). YouCat: Weakly Supervised Youtube Video Categorization System from Meta Data & User Comments using WordNet & Wikipedia. International Conference on Computational Linguistics. 1865–1882.2 indexed citations
15.
Bhattacharyya, Pushpak, et al.. (2012). Building Multilingual Search Index using open source framework. International Conference on Computational Linguistics. 201–210.3 indexed citations
16.
Bhattacharyya, Pushpak, et al.. (2012). Domain Specific Ontology Extractor For Indian Languages. International Conference on Computational Linguistics. 75–84.7 indexed citations
17.
Bhattacharyya, Pushpak, et al.. (2011). Clause-Based Reordering Constraints to Improve Statistical Machine Translation. International Joint Conference on Natural Language Processing. 1351–1355.4 indexed citations
18.
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
Bellare, Kedar, et al.. (2004). Generic Text Summarization Using WordNet. Language Resources and Evaluation.21 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.