Antonio L. Lagarda
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition
- Language and Linguistics top 10%
- Information Systems
- Molecular Biology
- Co-authors
- Francisco CasacubertaEnrique VidalJorge CiveraJuan Miguel VilarSergio BarrachinaOliver BenderShahram KhadiviHermann Ney
- Topics
- Natural Language Processing Techniques (11 papers)Topic Modeling (10 papers)Speech and dialogue systems (5 papers)
In The Last Decade
Antonio L. Lagarda
11 papers receiving 205 citations
Peers
Comparison fields: 5 of 28
- Artificial Intelligence 233
- Computer Vision and Pattern Recognition 45
- Language and Linguistics 22
- Information Systems 21
- Molecular Biology 9
Countries citing papers authored by Antonio L. Lagarda
This map shows the geographic impact of Antonio L. Lagarda'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 Antonio L. Lagarda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Antonio L. Lagarda more than expected).
Fields of papers citing papers by Antonio L. Lagarda
This network shows the impact of papers produced by Antonio L. Lagarda. 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 Antonio L. Lagarda. The network helps show where Antonio L. Lagarda may publish in the future.
Co-authorship network of co-authors of Antonio L. Lagarda
This figure shows the co-authorship network connecting the top 25 collaborators of Antonio L. Lagarda. A scholar is included among the top collaborators of Antonio L. Lagarda 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 Antonio L. Lagarda. Antonio L. Lagarda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 14 | |
| 2 | Interactive Pattern Recognition and Human Language Technology for Digital Audiovisual Content Processing | 1 |
| 3 | 29 | |
| 4 | 23 | |
| 5 | Applying boosting to statistical machine translation. | 5 |
| 6 | 142 | |
| 7 | A Computer-Assisted Translation Tool based on Finite-State Technology | 1 |
| 8 | Finite-state models for computer assisted translation | 5 |
| 9 | From Machine Translation to Computer Assisted Translation using Finite-State Models | 15 |
| 10 | TransType2. Un sistema de ayuda a la traducción | 1 |
| 11 | Adapting finite-state translation to the TransType2 project | 7 |
| 12 | On the use of statistical machine-translation techniques within a memory-based translation system (AMETRA) | 1 |
About Antonio L. Lagarda
Antonio L. Lagarda is a scholar working on Artificial Intelligence, Language and Linguistics and Infectious Diseases, having authored 12 papers that have together received 244 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (11 papers), Topic Modeling (10 papers) and Speech and dialogue systems (5 papers). The work is most often cited by research in Artificial Intelligence (233 citations), Computer Vision and Pattern Recognition (45 citations) and Language and Linguistics (22 citations). Antonio L. Lagarda has collaborated with scholars based in Spain, Germany and Canada. Frequent co-authors include Francisco Casacuberta, Enrique Vidal, Jorge Civera, Juan Miguel Vilar, Sergio Barrachina, Oliver Bender, Shahram Khadivi, Hermann Ney, Jesús Tomás and Jorge González. Their work appears in journals such as Communications of the ACM, Computational Linguistics and Computer Speech & Language.
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.