Utpal Kumar Sikdar
- Artificial Intelligence top 2%
- Information Systems top 5%
- Signal Processing top 10%
- Social Psychology
- Communication top 10%
- Co-authors
- Björn GambäckAsif EkbalSriparna SahaMd Shad AkhtarMassimo PoesioOlga UryupinaM. HasanuzzamanErwin Marsi
- Topics
- Topic Modeling (24 papers)Natural Language Processing Techniques (20 papers)Biomedical Text Mining and Ontologies (10 papers)
In The Last Decade
Utpal Kumar Sikdar
28 papers receiving 512 citations
Hit Papers
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 521
- Information Systems 136
- Signal Processing 63
- Social Psychology 57
- Communication 53
Countries citing papers authored by Utpal Kumar Sikdar
This map shows the geographic impact of Utpal Kumar Sikdar'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 Utpal Kumar Sikdar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Utpal Kumar Sikdar more than expected).
Fields of papers citing papers by Utpal Kumar Sikdar
This network shows the impact of papers produced by Utpal Kumar Sikdar. 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 Utpal Kumar Sikdar. The network helps show where Utpal Kumar Sikdar may publish in the future.
Co-authorship network of co-authors of Utpal Kumar Sikdar
This figure shows the co-authorship network connecting the top 25 collaborators of Utpal Kumar Sikdar. A scholar is included among the top collaborators of Utpal Kumar Sikdar 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 Utpal Kumar Sikdar. Utpal Kumar Sikdar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 4 | |
| 3 | Using Convolutional Neural Networks to Classify Hate-Speechbreakdown → | 345 |
| 4 | 7 | |
| 5 | 7 | |
| 6 | 1 | |
| 7 | Feature-Rich Twitter Named Entity Recognition and Classification. | 10 |
| 8 | Twitter Named Entity Extraction and Linking Using Differential Evolution | 2 |
| 9 | 7 | |
| 10 | 9 | |
| 11 | 12 | |
| 12 | 2 | |
| 13 | 15 | |
| 14 | Adapting a State-of-the-art Anaphora Resolution System for Resource-poor Language | 7 |
| 15 | 4 | |
| 16 | 13 | |
| 17 | 1 | |
| 18 | Differential Evolution Based Feature Selection and Classifier Ensemble for Named Entity Recognition | 20 |
| 19 | 3 | |
| 20 | 4 |
About Utpal Kumar Sikdar
Utpal Kumar Sikdar is a scholar working on Artificial Intelligence, Management Science and Operations Research and Molecular Biology, having authored 29 papers that have together received 554 indexed citations. Recurring topics across this work include Topic Modeling (24 papers), Natural Language Processing Techniques (20 papers) and Biomedical Text Mining and Ontologies (10 papers). The work is most often cited by research in Artificial Intelligence (521 citations), Communication (53 citations) and Information Systems (136 citations). Utpal Kumar Sikdar has collaborated with scholars based in India, Italy and Norway. Frequent co-authors include Björn Gambäck, Asif Ekbal, Sriparna Saha, Md Shad Akhtar, Massimo Poesio, Olga Uryupina, Sriparna Saha, M. Hasanuzzaman, Erwin Marsi and Rune Sætre. Their work appears in journals such as Knowledge-Based Systems, Soft Computing and SpringerPlus.
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.