Philipp Koehn
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 5%
- Language and Linguistics top 2%
- Information Systems top 5%
- Molecular Biology
- Co-authors
- Christof MonzBarry HaddowYvette GrahamFrancisco CasacubertaVicent AlabauLuis A. LeivaDaniel Ortiz-MartínezMercedes García-Martínez
- Topics
- Natural Language Processing Techniques (6 papers)Topic Modeling (6 papers)Text Readability and Simplification (2 papers)
- Journals
- Integrative and Comparative BiologyInformationDublin City University Open Access Institutional Repository (Dublin City University)
- Partner nations
- United KingdomUnited StatesEstonia
In The Last Decade
Philipp Koehn
4 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 60
- Artificial Intelligence 2.1k
- Computer Vision and Pattern Recognition 256
- Language and Linguistics 191
- Information Systems 136
- Molecular Biology 114
Countries citing papers authored by Philipp Koehn
This map shows the geographic impact of Philipp Koehn'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 Philipp Koehn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philipp Koehn more than expected).
Fields of papers citing papers by Philipp Koehn
This network shows the impact of papers produced by Philipp Koehn. 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 Philipp Koehn. The network helps show where Philipp Koehn may publish in the future.
Co-authorship network of co-authors of Philipp Koehn
This figure shows the co-authorship network connecting the top 25 collaborators of Philipp Koehn. A scholar is included among the top collaborators of Philipp Koehn 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 Philipp Koehn. Philipp Koehn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | Findings of the 2017 Conference on Machine Translation (WMT17)breakdown → | 219 |
| 5 | 35 | |
| 6 | 3 | |
| 7 | Europarl: A Parallel Corpus for Statistical Machine Translationbreakdown → | 1913 |
About Philipp Koehn
Philipp Koehn is a scholar working on Linguistics and Language, Artificial Intelligence and Language and Linguistics, having authored 7 papers that have together received 2.2k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (6 papers), Topic Modeling (6 papers) and Text Readability and Simplification (2 papers). The work is most often cited by research in Artificial Intelligence (2.1k citations), Language and Linguistics (191 citations) and Computer Vision and Pattern Recognition (256 citations). Philipp Koehn has collaborated with scholars based in United Kingdom, United States and Estonia. Frequent co-authors include Christof Monz, Barry Haddow, Yvette Graham, Francisco Casacuberta, Vicent Alabau, Luis A. Leiva, Daniel Ortiz-Martínez, Mercedes García-Martínez, Hervé Saint-Amand and Michaël Carl. Their work appears in journals such as Integrative and Comparative Biology, Information and Dublin City University Open Access Institutional Repository (Dublin City University).
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.