Timothy Baldwin
About
In The Last Decade
Timothy Baldwin
119 papers receiving 4.0k citations
Hit Papers
Peers
Comparison fields: 5 of 189
- Artificial Intelligence 2.0k
- Molecular Biology 1.3k
- Information Systems 612
- Cardiology and Cardiovascular Medicine 599
- Cellular and Molecular Neuroscience 588
Countries citing papers authored by Timothy Baldwin
This map shows the geographic impact of Timothy Baldwin'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 Timothy Baldwin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timothy Baldwin more than expected).
Fields of papers citing papers by Timothy Baldwin
This network shows the impact of papers produced by Timothy Baldwin. 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 Timothy Baldwin. The network helps show where Timothy Baldwin may publish in the future.
Co-authorship network of co-authors of Timothy Baldwin
This figure shows the co-authorship network connecting the top 25 collaborators of Timothy Baldwin. A scholar is included among the top collaborators of Timothy Baldwin 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 Timothy Baldwin. Timothy Baldwin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 2 | |
| 3 | 9 | |
| 4 | 2 | |
| 5 | 6 | |
| 6 | 7 | |
| 7 | Detecting Chemical Reactions in Patents | 3 |
| 8 | 121 | |
| 9 | Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields | 2 |
| 10 | 58 | |
| 11 | Is all that Glitters in Machine Translation Quality Estimation really Gold | 8 |
| 12 | CQADupStack: A benchmark data set for community question-answering research | 12 |
| 13 | Novel Word-sense Identification | 19 |
| 14 | unimelb: Topic Modelling-based Word Sense Induction for Web Snippet Clustering | 15 |
| 15 | The Utility of Discourse Structure in Identifying Resolved Threads in Technical User Forums | 4 |
| 16 | On-line Trend Analysis with Topic Models: #twitter Trends Detection Topic Model Online | 112 |
| 17 | Geolocation Prediction in Social Media Data by Finding Location Indicative Words | 107 |
| 18 | Improving Parsing and PP Attachment Performance with Sense Information | 43 |
| 19 | 6 | |
| 20 | Improving Dictionary Accessibility by Maximizing Use of Available Knowledge | 1 |
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.