Dilek Hakkani-Tür
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Signal Processing top 10%
- Information Systems
- Experimental and Cognitive Psychology
- Co-authors
- Gökhan TürGiuseppe RiccardiFrédéric BéchetElizabeth ShribergAslı ÇelikyılmazAndreas StolckeSébastien CuendetRobinson Piramuthu
- Topics
- Natural Language Processing Techniques (23 papers)Topic Modeling (21 papers)Speech and dialogue systems (13 papers)
- Journals
- ACM Computing SurveysIEEE Signal Processing MagazineIEEE Transactions on Audio Speech and Language Processing
- Partner nations
- United StatesUnited KingdomAlgeria
In The Last Decade
Dilek Hakkani-Tür
30 papers receiving 423 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 405
- Computer Vision and Pattern Recognition 83
- Signal Processing 44
- Information Systems 33
- Experimental and Cognitive Psychology 28
Countries citing papers authored by Dilek Hakkani-Tür
This map shows the geographic impact of Dilek Hakkani-Tür'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 Dilek Hakkani-Tür with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dilek Hakkani-Tür more than expected).
Fields of papers citing papers by Dilek Hakkani-Tür
This network shows the impact of papers produced by Dilek Hakkani-Tür. 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 Dilek Hakkani-Tür. The network helps show where Dilek Hakkani-Tür may publish in the future.
Co-authorship network of co-authors of Dilek Hakkani-Tür
This figure shows the co-authorship network connecting the top 25 collaborators of Dilek Hakkani-Tür. A scholar is included among the top collaborators of Dilek Hakkani-Tür 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 Dilek Hakkani-Tür. Dilek Hakkani-Tür is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 10 | |
| 4 | 3 | |
| 5 | 6 | |
| 6 | 2 | |
| 7 | 24 | |
| 8 | 1 | |
| 9 | 2 | |
| 10 | 2 | |
| 11 | Research Challenges and Opportunities in Mobile Applications | 5 |
| 12 | LDA Based Similarity Modeling for Question Answering | 33 |
| 13 | A Graph-Based Semi-Supervised Learning for Question Semantic Labeling | 2 |
| 14 | 11 | |
| 15 | 4 | |
| 16 | 15 | |
| 17 | 15 | |
| 18 | 5 | |
| 19 | 9 | |
| 20 | 8 |
About Dilek Hakkani-Tür
Dilek Hakkani-Tür is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 32 papers that have together received 468 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (23 papers), Topic Modeling (21 papers) and Speech and dialogue systems (13 papers). The work is most often cited by research in Artificial Intelligence (405 citations), Signal Processing (44 citations) and Computer Vision and Pattern Recognition (83 citations). Dilek Hakkani-Tür has collaborated with scholars based in United States, United Kingdom and Algeria. Frequent co-authors include Gökhan Tür, Giuseppe Riccardi, Frédéric Béchet, Elizabeth Shriberg, Aslı Çelikyılmaz, Andreas Stolcke, Sébastien Cuendet, Robinson Piramuthu, Jesse Thomason and Spandana Gella. Their work appears in journals such as ACM Computing Surveys, IEEE Signal Processing Magazine and IEEE Transactions on Audio Speech and Language Processing.
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.