Danushka Bollegala
- Artificial Intelligence top 0.5%
- Information Systems top 2%
- Molecular Biology
- Computer Vision and Pattern Recognition top 10%
- Computational Theory and Mathematics top 5%
- Co-authors
- Mitsuru IshizukaJohn M. CarrollDavid WeirYutaka MatsuoMasahiro KanekoNaoaki OkazakiKatie AtkinsonTrevor Bench‐Capon
- Topics
- Topic Modeling (89 papers)Natural Language Processing Techniques (66 papers)Text and Document Classification Technologies (26 papers)
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEInformation Sciences
- Partner nations
- United KingdomJapanUnited States
In The Last Decade
Danushka Bollegala
117 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 131
- Artificial Intelligence 1.5k
- Information Systems 350
- Molecular Biology 168
- Computer Vision and Pattern Recognition 121
- Computational Theory and Mathematics 117
Countries citing papers authored by Danushka Bollegala
This map shows the geographic impact of Danushka Bollegala'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 Danushka Bollegala with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danushka Bollegala more than expected).
Fields of papers citing papers by Danushka Bollegala
This network shows the impact of papers produced by Danushka Bollegala. 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 Danushka Bollegala. The network helps show where Danushka Bollegala may publish in the future.
Co-authorship network of co-authors of Danushka Bollegala
This figure shows the co-authorship network connecting the top 25 collaborators of Danushka Bollegala. A scholar is included among the top collaborators of Danushka Bollegala 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 Danushka Bollegala. Danushka Bollegala 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 | 0 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 18 | |
| 6 | Correcting crowdsourced annotations to improve detection of outcome types in evidence based medicine | 3 |
| 7 | Extracting Supporting Evidence from Medical Negligence Claim Texts. | 1 |
| 8 | 30 | |
| 9 | Semi-Supervised Multi-Task Word Embeddings. | 0 |
| 10 | An Empirical Study on Fine-Grained Named Entity Recognition | 16 |
| 11 | Effect of Data Imbalance on Unsupervised Domain Adaptation of Part-of-Speech Tagging and Pivot Selection Strategies. | 2 |
| 12 | Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection. | 2 |
| 13 | An Optimality Proof for the PairDiff operator for Representing Relations between Words. | 1 |
| 14 | BS-1-12 Analyzing Patterns from Twitter Happiness referring Stock Market and American Unemployment Rate Announcement | 1 |
| 15 | Similarity is not entailment: jointly learning similarity transformations for textual entailment | 5 |
| 16 | Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification | 84 |
| 17 | A Semi-Supervised Approach to Improve Classification of Infrequent Discourse Relations Using Feature Vector Extension | 29 |
| 18 | Towards Semi-Supervised Classification of Discourse Relations using Feature Correlations | 3 |
| 19 | WebSim: A Web-based Semantic Similarity Measure | 12 |
| 20 | Disambiguating Personal Names on the Web using Automatically Extracted Key Phrases | 26 |
About Danushka Bollegala
Danushka Bollegala is a scholar working on Artificial Intelligence, Management Science and Operations Research and Information Systems, having authored 131 papers that have together received 2.0k indexed citations. Recurring topics across this work include Topic Modeling (89 papers), Natural Language Processing Techniques (66 papers) and Text and Document Classification Technologies (26 papers). The work is most often cited by research in Artificial Intelligence (1.5k citations), Toxicology (72 citations) and Health Informatics (22 citations). Danushka Bollegala has collaborated with scholars based in United Kingdom, Japan and United States. Frequent co-authors include Mitsuru Ishizuka, John M. Carroll, David Weir, Yutaka Matsuo, Masahiro Kaneko, Naoaki Okazaki, Katie Atkinson, Yutaka Matsuo, Trevor Bench‐Capon and John Y. Goulermas. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Information Sciences.
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.