Qun Liu
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 5%
- Molecular Biology
- Computer Networks and Communications top 10%
- Topics
- Natural Language Processing Techniques (63 papers)Topic Modeling (57 papers)Speech and dialogue systems (9 papers)
In The Last Decade
Qun Liu
87 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 99
- Artificial Intelligence 1.4k
- Computer Vision and Pattern Recognition 240
- Information Systems 199
- Molecular Biology 127
- Computer Networks and Communications 113
Countries citing papers authored by Qun Liu
This map shows the geographic impact of Qun Liu'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 Qun Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qun Liu more than expected).
Fields of papers citing papers by Qun Liu
This network shows the impact of papers produced by Qun Liu. 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 Qun Liu. The network helps show where Qun Liu may publish in the future.
Co-authorship network of co-authors of Qun Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Qun Liu. A scholar is included among the top collaborators of Qun Liu 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 Qun Liu. Qun Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 1 | |
| 3 | DynaBERT: Dynamic BERT with Adaptive Width and Depth | 27 |
| 4 | 8 | |
| 5 | 3 | |
| 6 | Syntax-based deep matching of short texts | 26 |
| 7 | Joint learning of constituency and dependency grammars by decomposed cross-lingual induction | 1 |
| 8 | Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Tutorial Abstracts | 7 |
| 9 | 8 | |
| 10 | A Topic-Triggered Language Model for Statistical Machine Translation | 3 |
| 11 | Summary on CWMT2011 MT Translation Evaluation | 5 |
| 12 | A novel dependency-to-string model for statistical machine translation | 34 |
| 13 | ETS: An Error Tolerable System for Coreference Resolution | 1 |
| 14 | Extracting Hierarchical Rules from a Weighted Alignment Matrix | 5 |
| 15 | Fast Generation of Translation Forest for Large-Scale SMT Discriminative Training | 7 |
| 16 | Learning Lexicalized Reordering Models from Reordering Graphs | 2 |
| 17 | SMT Domain Adaptation Based on Monolingual Context Information | 1 |
| 18 | The ICT Statistical Machine Translation Systems for IWSLT 2007 | 2 |
| 19 | Example Based Chinese-Mongolian Machine Translation | 0 |
| 20 | An EBMT system based on word alignment. | 2 |
About Qun Liu
Qun Liu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 99 papers that have together received 1.6k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (63 papers), Topic Modeling (57 papers) and Speech and dialogue systems (9 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Computer Vision and Pattern Recognition (240 citations) and Information Systems (199 citations). Qun Liu has collaborated with scholars based in China, Ireland and Sweden. Frequent co-authors include Deyi Xiong, Shouxun Lin, Hongkui Yu, Huaping Zhang, Sujian Li, Zhaopeng Tu, Longyue Wang, Andy Way, Yang Liu and Xiaofeng Liao. Their work appears in journals such as Organic Letters, Information Sciences and IEEE Transactions on Knowledge and Data Engineering.
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.