Chris Callison-Burch
Impact in
- Artificial Intelligence top 0.02%
- Natural Language Processing Techniques
- Topic Modeling
- Text Readability and Simplification
- Speech and dialogue systems
- Semantic Web and Ontologies
- Advanced Text Analysis Techniques
- Speech Recognition and Synthesis
- Computer Science Applications top 0.2%
Papers in
-
- Topic Modeling 171
- Natural Language Processing Techniques 162
- Text Readability and Simplification 31
- Advanced Text Analysis Techniques 20
- Speech and dialogue systems 18
- Algorithms and Data Compression 11
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- Multimodal Machine Learning Applications 17
- Co-authors
- Philipp Koehn (27 shared papers)Omar F. Zaidan (14 shared papers)Alexandra Birch (8 shared papers)Chris Dyer (7 shared papers)Ondřej Bojar (5 shared papers)Miles Osborne (14 shared papers)Hieu Hoang (4 shared papers)Nicola Bertoldi (3 shared papers)
- Journals
- Computational Linguistics (5 papers)Transactions of the Association for Computational Linguistics (4 papers)Language Resources and Evaluation (3 papers)International Journal of Medical Informatics (2 papers)PLoS ONE (2 papers)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Chris Callison-Burch
208 papers receiving 12.5k citations
Chris Callison-Burch's Hit Papers
Peers
Comparison fields: 5 of 151
- Artificial Intelligence 12.9k
- Computer Science Applications 824
- Computer Vision and Pattern Recognition 1.8k
- Health Informatics 77
- Language and Linguistics 537
Countries citing papers authored by Chris Callison-Burch
This map shows the geographic impact of Chris Callison-Burch'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 Chris Callison-Burch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris Callison-Burch more than expected).
Fields of papers citing papers by Chris Callison-Burch
This network shows the impact of papers produced by Chris Callison-Burch. 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 Chris Callison-Burch. The network helps show where Chris Callison-Burch may publish in the future.
Co-authors
The 25 scholars most cited alongside Chris Callison-Burch, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 220 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Moses Hit paper breakdown → | 2007 | 3479 |
| 2 | Moses: Open Source Toolkit for Statistical Machine Translation Hit paper breakdown → | 2007 | 1145 |
| 3 | PPDB: The Paraphrase Database Hit paper breakdown → | 2013 | 429 |
| 4 | Re-evaluating the Role of Bleu in Machine Translation Research Hit paper breakdown → | 2006 | 419 |
| 5 | 2005 | 399 | |
| 6 | 2009 | 341 | |
| 7 | A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk Hit paper breakdown → | 2018 | 335 |
| 8 | Optimizing Statistical Machine Translation for Text Simplification Hit paper breakdown → | 2016 | 330 |
| 9 | 2007 | 277 | |
| 10 | Edinburgh System Description for the 2005 IWSLT Speech Translation Evaluation | 2005 | 267 |
| 11 | 2015 | 262 | |
| 12 | 2009 | 261 | |
| 13 | 2013 | 229 | |
| 14 | Creating Speech and Language Data With Amazon's Mechanical Turk | 2010 | 221 |
| 15 | 2006 | 220 | |
| 16 | Crowdsourcing Translation: Professional Quality from Non-Professionals | 2011 | 216 |
| 17 | 2008 | 196 | |
| 18 | Findings of the 2013 Workshop on Statistical Machine Translation | 2013 | 186 |
| 19 | 2015 | 184 | |
| 20 | Deduplicating Training Data Makes Language Models Better Hit paper breakdown → | 2022 | 171 |
About Chris Callison-Burch
Chris Callison-Burch is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science Applications, Information Systems and Molecular Biology, having authored 220 papers that have together received 14.3k indexed citations. Recurring topics across this work include Topic Modeling (171 papers), Natural Language Processing Techniques (162 papers), Text Readability and Simplification (31 papers), Advanced Text Analysis Techniques (20 papers), Speech and dialogue systems (18 papers), Mobile Crowdsensing and Crowdsourcing (18 papers), Multimodal Machine Learning Applications (17 papers) and Algorithms and Data Compression (11 papers). The work is most often cited by research in Artificial Intelligence (12.9k citations), Computer Science Applications (824 citations), Computer Vision and Pattern Recognition (1.8k citations), Health Informatics (77 citations) and Language and Linguistics (537 citations). Chris Callison-Burch has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Philipp Koehn, Omar F. Zaidan, Alexandra Birch, Chris Dyer, Ondřej Bojar, Miles Osborne, Hieu Hoang, Nicola Bertoldi, Marcello Federico and Richard Zens. Their work appears in journals such as Computational Linguistics, Transactions of the Association for Computational Linguistics, Language Resources and Evaluation, International Journal of Medical Informatics and PLoS ONE.
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.