Claudiu Musat
- Artificial Intelligence top 5%
- Topic Modeling 11
- Sentiment Analysis and Opinion Mining 10
- Advanced Text Analysis Techniques 8
- Natural Language Processing Techniques 4
- Text and Document Classification Technologies 3
- Speech and dialogue systems 2
- Information Systems top 10%
- Information Retrieval and Search Behavior 2
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- Stock Market Forecasting Methods 2
Claudiu Musat
18 papers receiving 321 citations
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 303
- Computer Vision and Pattern Recognition 78
- Information Systems 54
- Statistical and Nonlinear Physics 22
- Computer Science Applications 8
Countries citing papers authored by Claudiu Musat
This map shows the geographic impact of Claudiu Musat'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 Claudiu Musat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Claudiu Musat more than expected).
Fields of papers citing papers by Claudiu Musat
This network shows the impact of papers produced by Claudiu Musat. 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 Claudiu Musat. The network helps show where Claudiu Musat may publish in the future.
Co-authorship network
The 24 scholars most cited alongside Claudiu Musat, 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 | 1 | |
| 2 | 2021 | 3 | |
| 3 | Evaluating The Search Phase of Neural Architecture Search | 2020 | 55 |
| 4 | T-RECS: a Transformer-based Recommender Generating Textual Explanations and Integrating Unsupervised Language-based Critiquing. | 2020 | 2 |
| 5 | 2020 | 7 | |
| 6 | Automatic Creation of Text Corpora for Low-Resource Languages from the Internet: The Case of Swiss German | 2019 | 2 |
| 7 | 2019 | 5 | |
| 8 | 2019 | 9 | |
| 9 | EmbedRank: Unsupervised Keyphrase Extraction using Sentence Embeddings. | 2018 | 13 |
| 10 | 2018 | 120 | |
| 11 | 2018 | 11 | |
| 12 | Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German | 2017 | 3 |
| 13 | 2017 | 39 | |
| 14 | 2014 | 2 | |
| 15 | 2014 | 10 | |
| 16 | 2014 | 23 | |
| 17 | 2014 | 4 | |
| 18 | Fine-Grained Emotion Recognition in Olympic Tweets Based on Human Computation | 2013 | 18 |
| 19 | A novel human computation game for critique aggregation | 2013 | 4 |
| 20 | 2012 | 12 |
About Claudiu Musat
Claudiu Musat is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Human-Computer Interaction, having authored 20 papers that have together received 343 indexed citations. Recurring topics across this work include Topic Modeling (11 papers), Sentiment Analysis and Opinion Mining (10 papers), Advanced Text Analysis Techniques (8 papers), Natural Language Processing Techniques (4 papers), Text and Document Classification Technologies (3 papers), Information Retrieval and Search Behavior (2 papers), Speech and dialogue systems (2 papers) and Stock Market Forecasting Methods (2 papers). The work is most often cited by research in Artificial Intelligence (303 citations), Computer Vision and Pattern Recognition (78 citations) and Information Systems (54 citations). Claudiu Musat has collaborated with scholars based in Switzerland, France and Australia. Frequent co-authors include Michael Baeriswyl, Theus Hossmann, Martin Jaggi, Pearl Pu, Mathieu Salzmann, Kaicheng Yu, Athanasios Giannakopoulos, Boi Faltings, Alireza Ghasemi and Robert West. Their work appears in journals such as Neurocomputing, Archives of Oral Biology and Language Resources and Evaluation.
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.