Didier Schwab
- Artificial Intelligence top 10%
- Signal Processing
- Information Systems
- Computer Vision and Pattern Recognition
- Language and Linguistics
- Co-authors
- El Moatez Billah NagoudiMathieu LafourcadeLaurent BesacierHang LeJiatao GuViolaine PrinceJuan PinoChanghan Wang
- Topics
- Natural Language Processing Techniques (31 papers)Topic Modeling (25 papers)Speech and dialogue systems (8 papers)
In The Last Decade
Didier Schwab
37 papers receiving 202 citations
Peers
Comparison fields: 5 of 35
- Artificial Intelligence 194
- Signal Processing 26
- Information Systems 23
- Computer Vision and Pattern Recognition 15
- Language and Linguistics 11
Countries citing papers authored by Didier Schwab
This map shows the geographic impact of Didier Schwab'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 Didier Schwab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Didier Schwab more than expected).
Fields of papers citing papers by Didier Schwab
This network shows the impact of papers produced by Didier Schwab. 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 Didier Schwab. The network helps show where Didier Schwab may publish in the future.
Co-authorship network of co-authors of Didier Schwab
This figure shows the co-authorship network connecting the top 25 collaborators of Didier Schwab. A scholar is included among the top collaborators of Didier Schwab 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 Didier Schwab. Didier Schwab is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | Simplification Strategies in French Spontaneous Speech | 1 |
| 5 | 4 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 0 | |
| 9 | 1 | |
| 10 | 34 | |
| 11 | 9 | |
| 12 | 16 | |
| 13 | Uniformisation de corpus anglais annotés en sens | 1 |
| 14 | Évaluation et consolidation d'un réseau lexical grâce à un assistant ludique pour le " mot sur le bout de la langue " | 3 |
| 15 | 0 | |
| 16 | 3 | |
| 17 | Lexical Functions for Ants Based Semantic Analysis | 1 |
| 18 | Vecteurs conceptuels et structuration émergente de terminologies | 3 |
| 19 | Vers l'apprentissage automatique, pour et par les vecteurs conceptuels, de fonctions lexicales. L'exemple de l'antonymie. | 1 |
| 20 | Amélioration de la représentation sémantique lexicale par les vecteurs conceptuels : le rôle de l'antonymie | 1 |
About Didier Schwab
Didier Schwab is a scholar working on Artificial Intelligence, Language and Linguistics and Linguistics and Language, having authored 49 papers that have together received 221 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (31 papers), Topic Modeling (25 papers) and Speech and dialogue systems (8 papers). The work is most often cited by research in Artificial Intelligence (194 citations), Signal Processing (26 citations) and Health Informatics (3 citations). Didier Schwab has collaborated with scholars based in France, Algeria and Italy. Frequent co-authors include El Moatez Billah Nagoudi, Mathieu Lafourcade, Laurent Besacier, Hang Le, Jiatao Gu, Violaine Prince, Juan Pino, Changhan Wang, Michael Zock and Olivier Ferret. Their work appears in journals such as Biomedical Signal Processing and Control, Lecture notes in computer science and The Journal of Deaf Studies and Deaf Education.
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.