Yves Schabes
- Artificial Intelligence top 0.5%
- Computational Theory and Mathematics top 2%
- Language and Linguistics top 2%
- Information Systems top 10%
- Molecular Biology
- Co-authors
- Aravind K. JoshiStuart M. ShieberAnne AbeilléFernando PereiraRichard C. WatersFernando C. N. PereiraSharon CoteAndrew R. Golding
- Topics
- Natural Language Processing Techniques (31 papers)Topic Modeling (14 papers)semigroups and automata theory (12 papers)
- Partner nations
- United StatesJapanFrance
In The Last Decade
Yves Schabes
37 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 50
- Artificial Intelligence 1.8k
- Computational Theory and Mathematics 311
- Language and Linguistics 246
- Information Systems 96
- Molecular Biology 78
Countries citing papers authored by Yves Schabes
This map shows the geographic impact of Yves Schabes'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 Yves Schabes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yves Schabes more than expected).
Fields of papers citing papers by Yves Schabes
This network shows the impact of papers produced by Yves Schabes. 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 Yves Schabes. The network helps show where Yves Schabes may publish in the future.
Co-authorship network of co-authors of Yves Schabes
This figure shows the co-authorship network connecting the top 25 collaborators of Yves Schabes. A scholar is included among the top collaborators of Yves Schabes 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 Yves Schabes. Yves Schabes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Speech Recognition by Composition of Weighted Finite Automata | 2 |
| 2 | Finite-State Morphology: Inflections and Derivations in a Single Framework Using Dictionaries and Rules | 3 |
| 3 | 91 | |
| 4 | 183 | |
| 5 | Tree insertion grammar: a cubic-time, parsable formalism that lexicalizes context-free grammar without changing the trees produced | 106 |
| 6 | 22 | |
| 7 | 11 | |
| 8 | 23 | |
| 9 | 190 | |
| 10 | 63 | |
| 11 | 91 | |
| 12 | 75 | |
| 13 | 17 | |
| 14 | 19 | |
| 15 | 52 | |
| 16 | Mathematical and computational aspects of lexicalized grammars | 115 |
| 17 | 2 | |
| 18 | A Lexicalized Tree Adjoining Grammar for English | 214 |
| 19 | The Relevance of Lexicalization to Parsing. | 7 |
| 20 | 43 |
About Yves Schabes
Yves Schabes is a scholar working on Artificial Intelligence, Language and Linguistics and Computational Theory and Mathematics, having authored 37 papers that have together received 1.9k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (31 papers), Topic Modeling (14 papers) and semigroups and automata theory (12 papers). The work is most often cited by research in Artificial Intelligence (1.8k citations), Language and Linguistics (246 citations) and Computational Theory and Mathematics (311 citations). Yves Schabes has collaborated with scholars based in United States, Japan and France. Frequent co-authors include Aravind K. Joshi, Stuart M. Shieber, Anne Abeillé, Fernando Pereira, Richard C. Waters, Fernando C. N. Pereira, Sharon Cote, Andrew R. Golding, K. Vijay‐Shanker and Michal Roth. Their work appears in journals such as Fuzzy Sets and Systems, Computer and IEEE Multimedia.
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.