Thomas Lin
- Artificial Intelligence top 5%
- Information Systems top 10%
- Molecular Biology
- Management Science and Operations Research top 10%
- Computer Vision and Pattern Recognition
- Co-authors
- Oren EtzioniMichael GamonPatrick PantelAsma Ben AbachaWen-wai YimAnitha KannanAriel FuxmanGagandeep Singh
- Topics
- Topic Modeling (13 papers)Natural Language Processing Techniques (9 papers)Biomedical Text Mining and Ontologies (5 papers)
- Journals
- NeuroImageScientific DataEmpirical Methods in Natural Language Processing
- Partner nations
- United StatesAustriaUnited Kingdom
In The Last Decade
Thomas Lin
17 papers receiving 375 citations
Peers
Comparison fields: 5 of 45
- Artificial Intelligence 335
- Information Systems 94
- Molecular Biology 51
- Management Science and Operations Research 45
- Computer Vision and Pattern Recognition 28
Countries citing papers authored by Thomas Lin
This map shows the geographic impact of Thomas Lin'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 Thomas Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Lin more than expected).
Fields of papers citing papers by Thomas Lin
This network shows the impact of papers produced by Thomas Lin. 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 Thomas Lin. The network helps show where Thomas Lin may publish in the future.
Co-authorship network of co-authors of Thomas Lin
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Lin. A scholar is included among the top collaborators of Thomas Lin 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 Thomas Lin. Thomas Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 26 | |
| 3 | 5 | |
| 4 | 26 | |
| 5 | 16 | |
| 6 | 34 | |
| 7 | Mining Entity Types from Query Logs via User Intent Modeling | 34 |
| 8 | Entity Linking at Web Scale | 58 |
| 9 | No Noun Phrase Left Behind: Detecting and Typing Unlinkable Entities | 58 |
| 10 | 15 | |
| 11 | 55 | |
| 12 | Commonsense from the Web: Relation Properties | 3 |
| 13 | Machine Reading at the University of Washington | 22 |
| 14 | Identifying Functional Relations in Web Text | 22 |
| 15 | 12 | |
| 16 | 3 | |
| 17 | 2 |
About Thomas Lin
Thomas Lin is a scholar working on Artificial Intelligence, Health Information Management and Information Systems, having authored 17 papers that have together received 393 indexed citations. Recurring topics across this work include Topic Modeling (13 papers), Natural Language Processing Techniques (9 papers) and Biomedical Text Mining and Ontologies (5 papers). The work is most often cited by research in Health Informatics (19 citations), Artificial Intelligence (335 citations) and Information Systems (94 citations). Thomas Lin has collaborated with scholars based in United States, Austria and United Kingdom. Frequent co-authors include Oren Etzioni, Michael Gamon, Patrick Pantel, Asma Ben Abacha, Wen-wai Yim, Anitha Kannan, Ariel Fuxman, Gagandeep Singh, Jeff Huang and Meliha Yetişgen. Their work appears in journals such as NeuroImage, Scientific Data and Empirical Methods in Natural Language 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.