Mark Fishel
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 10%
- Language and Linguistics top 5%
- Information Systems
- Molecular Biology
- Co-authors
- Ondřej BojarChristian FedermannSantanu PalPhilipp KoehnMatt PostMathias MüllerChristof MonzBarry Haddow
- Topics
- Natural Language Processing Techniques (40 papers)Topic Modeling (35 papers)Text Readability and Simplification (10 papers)
- Journals
- SHILAP Revista de lepidopterologíaLanguage Resources and EvaluationIEEE Multimedia
- Partner nations
- EstoniaSwitzerlandCzechia
In The Last Decade
Mark Fishel
42 papers receiving 526 citations
Hit Papers
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 546
- Computer Vision and Pattern Recognition 138
- Language and Linguistics 62
- Information Systems 39
- Molecular Biology 28
Countries citing papers authored by Mark Fishel
This map shows the geographic impact of Mark Fishel'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 Mark Fishel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Fishel more than expected).
Fields of papers citing papers by Mark Fishel
This network shows the impact of papers produced by Mark Fishel. 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 Mark Fishel. The network helps show where Mark Fishel may publish in the future.
Co-authorship network of co-authors of Mark Fishel
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Fishel. A scholar is included among the top collaborators of Mark Fishel 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 Mark Fishel. Mark Fishel 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 | 0 | |
| 3 | 3 | |
| 4 | 9 | |
| 5 | Findings of the 2019 Conference on Machine Translation (WMT19)breakdown → | 256 |
| 6 | Machine Translation for Subtitling: A Large-Scale Evaluation | 20 |
| 7 | 6 | |
| 8 | 31 | |
| 9 | 1 | |
| 10 | 1 | |
| 11 | 3 | |
| 12 | Automatic MT Error Analysis: Hjerson Helping Addicter | 5 |
| 13 | 3 | |
| 14 | 13 | |
| 15 | 9 | |
| 16 | 7 | |
| 17 | 23 | |
| 18 | Linguistically Motivated Unsupervised Segmentation for Machine Translation. | 9 |
| 19 | Voting and Stacking in Data-Driven Dependency Parsing | 3 |
| 20 | Experiments on Processing Overlapping Parallel Corpora | 1 |
About Mark Fishel
Mark Fishel is a scholar working on Artificial Intelligence, Language and Linguistics and Computer Vision and Pattern Recognition, having authored 46 papers that have together received 595 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (40 papers), Topic Modeling (35 papers) and Text Readability and Simplification (10 papers). The work is most often cited by research in Artificial Intelligence (546 citations), Computer Vision and Pattern Recognition (138 citations) and Language and Linguistics (62 citations). Mark Fishel has collaborated with scholars based in Estonia, Switzerland and Czechia. Frequent co-authors include Ondřej Bojar, Christian Federmann, Santanu Pal, Philipp Koehn, Matt Post, Mathias Müller, Christof Monz, Barry Haddow, Matthias Huck and Yvette Graham. Their work appears in journals such as SHILAP Revista de lepidopterología, Language Resources and Evaluation 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.