Avia Efrat
Impact in
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- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Speech Recognition and Synthesis
- Text Readability and Simplification
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Image Retrieval and Classification Techniques
Papers in
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- Natural Language Processing Techniques 5
- Topic Modeling 4
- Text Readability and Simplification 3
- Advanced Text Analysis Techniques 1
- Algorithms and Data Compression 1
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- Second Language Acquisition and Learning 1
- Co-authors
- Omer Levy (4 shared papers)Uri Shaham (3 shared papers)Jonathan Berant (2 shared papers)Or Honovich (1 shared paper)Mor Geva (1 shared paper)Ankit Gupta (1 shared paper)Wenhan Xiong (1 shared paper)Elad Segal (1 shared paper)
- Journals
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- IsraelUnited States
In The Last Decade
Avia Efrat
5 papers receiving 45 citations
Peers
Comparison fields: 5 of 16
- Artificial Intelligence 41
- Computer Vision and Pattern Recognition 12
- Software 2
- Information Systems 3
- Computer Networks and Communications 3
Countries citing papers authored by Avia Efrat
This map shows the geographic impact of Avia Efrat'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 Avia Efrat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Avia Efrat more than expected).
Fields of papers citing papers by Avia Efrat
This network shows the impact of papers produced by Avia Efrat. 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 Avia Efrat. The network helps show where Avia Efrat may publish in the future.
Co-authors
The 8 scholars most cited alongside Avia Efrat, 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 | 2022 | 27 | |
| 2 | 2023 | 9 | |
| 3 | 2023 | 7 | |
| 4 | 2021 | 4 | |
| 5 | Tag-based Multi-Span Extraction in Reading Comprehension. | 2019 | 2 |
About Avia Efrat
Avia Efrat is a scholar working on Artificial Intelligence, Developmental and Educational Psychology, Infectious Diseases, Organic Chemistry and Surgery, having authored 5 papers that have together received 49 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (5 papers), Topic Modeling (4 papers), Text Readability and Simplification (3 papers), Advanced Text Analysis Techniques (1 paper), Second Language Acquisition and Learning (1 paper) and Algorithms and Data Compression (1 paper). The work is most often cited by research in Artificial Intelligence (41 citations), Computer Vision and Pattern Recognition (12 citations), Software (2 citations), Information Systems (3 citations) and Computer Networks and Communications (3 citations). Avia Efrat has collaborated with scholars based in Israel and United States. Frequent co-authors include Omer Levy, Uri Shaham, Jonathan Berant, Or Honovich, Mor Geva, Ankit Gupta, Wenhan Xiong and Elad Segal. Their work appears in journals such as Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing and arXiv (Cornell University).
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.