Ergun Biçici
- Artificial Intelligence top 5%
- Natural Language Processing Techniques 33
- Topic Modeling 32
- Text Readability and Simplification 9
- Machine Learning and Algorithms 4
- Semantic Web and Ontologies 4
- Machine Learning and Data Classification 3
- Neural Networks and Applications 3
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- Image and Video Quality Assessment 5
- Co-authors
- Deniz YüretAndy WayJosef van GenabithQun LiuRobert St. AmantLucia SpeciaDeclan GrovesKashif Shah
- Journals
- SHILAP Revista de lepidopterología (3 papers)IEEE Access (1 paper)Neural Computing and Applications (1 paper)
In The Last Decade
Ergun Biçici
42 papers receiving 278 citations
Peers
Comparison fields: 5 of 22
- Artificial Intelligence 327
- Computer Vision and Pattern Recognition 40
- Language and Linguistics 11
- Molecular Biology 45
- Information Systems 14
Countries citing papers authored by Ergun Biçici
This map shows the geographic impact of Ergun Biçici'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 Ergun Biçici with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ergun Biçici more than expected).
Fields of papers citing papers by Ergun Biçici
This network shows the impact of papers produced by Ergun Biçici. 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 Ergun Biçici. The network helps show where Ergun Biçici may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Ergun Biçici, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 0 | |
| 4 | 2023 | 0 | |
| 5 | 2020 | 1 | |
| 6 | 2019 | 4 | |
| 7 | 2016 | 4 | |
| 8 | 2015 | 9 | |
| 9 | CNGL-CORE: Referential Translation Machines for Measuring Semantic Similarity | 2013 | 8 |
| 10 | CNGL: Grading Student Answers by Acts of Translation | 2013 | 6 |
| 11 | Referential Translation Machines for Quality Estimation | 2013 | 23 |
| 12 | Feature Decay Algorithms for Fast Deployment of Accurate Statistical Machine Translation Systems | 2013 | 14 |
| 13 | 2013 | 18 | |
| 14 | 2013 | 18 | |
| 15 | RegMT System for Machine Translation, System Combination, and Evaluation | 2011 | 10 |
| 16 | Instance Selection for Machine Translation using Feature Decay Algorithms | 2011 | 44 |
| 17 | L1 Regularized Regression for Reranking and System Combination in Machine Translation | 2010 | 5 |
| 18 | Adaptive Model Weighting and Transductive Regression for Predicting Best System Combinations | 2010 | 3 |
| 19 | Clustering Word Pairs to Answer Analogy Questions | 2008 | 2 |
| 20 | 2006 | 1 |
About Ergun Biçici
Ergun Biçici is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 49 papers that have together received 341 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (33 papers), Topic Modeling (32 papers), Text Readability and Simplification (9 papers), Image and Video Quality Assessment (5 papers), Machine Learning and Algorithms (4 papers), Semantic Web and Ontologies (4 papers), Machine Learning and Data Classification (3 papers) and Neural Networks and Applications (3 papers). The work is most often cited by research in Artificial Intelligence (327 citations), Computer Vision and Pattern Recognition (40 citations) and Language and Linguistics (11 citations). Ergun Biçici has collaborated with scholars based in Ireland, Türkiye and China. Frequent co-authors include Deniz Yüret, Andy Way, Josef van Genabith, Qun Liu, Robert St. Amant, Lucia Specia, Declan Groves, Kashif Shah, Eleftherios Avramidis and Süleyman S. Kozat. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Neural Computing and Applications.
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.