Jean Sénellart
- Artificial Intelligence top 2%
- Natural Language Processing Techniques 33
- Topic Modeling 25
- Quantum Information and Cryptography 8
- Neural Networks and Reservoir Computing 6
- Algorithms and Data Compression 6
- Semantic Web and Ontologies 5
- Text Readability and Simplification 4
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- Multimodal Machine Learning Applications 5
- Language and Linguistics top 10%
- Co-authors
- Philipp KoehnJosep CregoCatherine KobusHolger SchwenkGuillaume KleinP. SenellartVincent NguyenNiccolò Somaschi
- Partner nations
- FranceUnited KingdomAustralia
In The Last Decade
Jean Sénellart
43 papers receiving 535 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 571
- Computer Vision and Pattern Recognition 123
- Language and Linguistics 33
- Atomic and Molecular Physics, and Optics 73
- Information Systems 34
Countries citing papers authored by Jean Sénellart
This map shows the geographic impact of Jean Sénellart'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 Jean Sénellart with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jean Sénellart more than expected).
Fields of papers citing papers by Jean Sénellart
This network shows the impact of papers produced by Jean Sénellart. 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 Jean Sénellart. The network helps show where Jean Sénellart may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jean Sénellart, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 15 | |
| 2 | 2024 | 10 | |
| 3 | 2024 | 2 | |
| 4 | 2023 | 32 | |
| 5 | 2023 | 1 | |
| 6 | 2023 | 2 | |
| 7 | The OpenNMT Neural Machine Translation Toolkit: 2020 Edition | 2020 | 16 |
| 8 | 2020 | 35 | |
| 9 | Neural Network Architectures for Arabic Dialect Identification | 2018 | 7 |
| 10 | OpenNMT: Neural Machine Translation Toolkit | 2018 | 27 |
| 11 | 2018 | 4 | |
| 12 | 2017 | 69 | |
| 13 | 2017 | 79 | |
| 14 | Incremental Adaptation Using Translation Information and Post-Editing Analysis | 2012 | 9 |
| 15 | 2011 | 0 | |
| 16 | Fast Approximate String Matching with Suffix Arrays and A* Parsing | 2010 | 6 |
| 17 | Translation model adaptation for an Arabic/French news translation system by lightly-supervised training | 2009 | 21 |
| 18 | Selective addition of corpus-extracted phrasal lexical rules to a rule-based machine translation system | 2009 | 4 |
| 19 | Reconnaissance automatique des entrées du lexique-grammaire des phrases figées | 1998 | 8 |
| 20 | 1998 | 4 |
About Jean Sénellart
Jean Sénellart is a scholar working on Artificial Intelligence, Acoustics and Ultrasonics and Hardware and Architecture, having authored 48 papers that have together received 650 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (33 papers), Topic Modeling (25 papers), Quantum Information and Cryptography (8 papers), Neural Networks and Reservoir Computing (6 papers), Algorithms and Data Compression (6 papers), Semantic Web and Ontologies (5 papers), Multimodal Machine Learning Applications (5 papers) and Text Readability and Simplification (4 papers). The work is most often cited by research in Artificial Intelligence (571 citations), Computer Vision and Pattern Recognition (123 citations) and Language and Linguistics (33 citations). Jean Sénellart has collaborated with scholars based in France, United Kingdom and Australia. Frequent co-authors include Philipp Koehn, Josep Crego, Catherine Kobus, Holger Schwenk, Guillaume Klein, P. Senellart, Vincent Nguyen, Niccolò Somaschi, A. Lemaı̂tre and Alexander M. Rush. Their work appears in journals such as Nature Nanotechnology, Computer Physics Communications and Optica.
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.