Manoj Kumar Chinnakotla

23 papers receiving 515 citations

Peers

Manoj Kumar Chinnakotla
Comparison fields: 5 of 64
  • Artificial Intelligence 461
  • Information Systems 120
  • Computer Vision and Pattern Recognition 66
  • Experimental and Cognitive Psychology 63
  • Social Psychology 39
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Countries citing papers authored by Manoj Kumar Chinnakotla

Since Specialization
Citations

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

Fields of papers citing papers by Manoj Kumar Chinnakotla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Manoj Kumar Chinnakotla. 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 Manoj Kumar Chinnakotla. The network helps show where Manoj Kumar Chinnakotla may publish in the future.

Co-authorship network of co-authors of Manoj Kumar Chinnakotla

This figure shows the co-authorship network connecting the top 25 collaborators of Manoj Kumar Chinnakotla. A scholar is included among the top collaborators of Manoj Kumar Chinnakotla 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 Manoj Kumar Chinnakotla. Manoj Kumar Chinnakotla 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
#WorkIndexed citations
1 37
2 2
3 6
4 10
5 21
6
Discovery and Promotion of Subtopic Level High Quality Domains for Programming Queries in Web Search.
1
7 62
8 5
9
Hand in Glove: Deep Feature Fusion Network Architectures for Answer Quality Prediction in Community Question Answering
8
10 47
11
IIITH at BioASQ Challange 2015 Task 3b: Bio-Medical Question Answering System
7
12
Did you know?: mining interesting trivia for entities from wikipedia
7
13 27
14
Multilingual Pseudo-Relevance Feedback: Performance Study of Assisting Languages
5
15 10
16 12
17
Character Sequence Modeling for Transliteration
2
18
Language Modeling Based Local Set Re-ranking using Manual Relevance Feedback
1
19
Hindi and Marathi to English Cross Language Information Retrieval.
4
20
Hindi and Marathi to English Cross Language Information Retrieval at CLEF 2007.
5

About Manoj Kumar Chinnakotla

Manoj Kumar Chinnakotla is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition, having authored 23 papers that have together received 553 indexed citations. Recurring topics across this work include Topic Modeling (18 papers), Natural Language Processing Techniques (13 papers) and Expert finding and Q&A systems (5 papers). The work is most often cited by research in Artificial Intelligence (461 citations), Information Systems (120 citations) and Experimental and Cognitive Psychology (63 citations). Manoj Kumar Chinnakotla has collaborated with scholars based in India and United States. Frequent co-authors include Puneet Agrawal, Umang Gupta, Michel Galley, Ankush Chatterjee, Radhakrishnan Srikanth, Manish Shrivastava, Arpita Das, Radhika Mamidi, Pushpak Bhattacharyya and Vishal Gupta. Their work appears in journals such as Computers in Human Behavior, International Journal of Data Science and Analytics and ACM Transactions on Asian Language Information Processing.

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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