Bingquan Liu
About
In The Last Decade
Bingquan Liu
85 papers receiving 846 citations
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 615
- Information Systems 226
- Molecular Biology 121
- Computer Vision and Pattern Recognition 96
- Sociology and Political Science 54
Countries citing papers authored by Bingquan Liu
This map shows the geographic impact of Bingquan 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 Bingquan Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bingquan Liu more than expected).
Fields of papers citing papers by Bingquan Liu
This network shows the impact of papers produced by Bingquan 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 Bingquan Liu. The network helps show where Bingquan Liu may publish in the future.
Co-authorship network of co-authors of Bingquan Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Bingquan Liu. A scholar is included among the top collaborators of Bingquan 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 Bingquan Liu. Bingquan 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 | 4 | |
| 3 | 10 | |
| 4 | 3 | |
| 5 | 3 | |
| 6 | 8 | |
| 7 | 19 | |
| 8 | Enlarging drug dictionary with semi-supervised learning for Drug Entity Recognition | 1 |
| 9 | 37 | |
| 10 | VRCA: a clustering algorithm for massive amount of texts | 2 |
| 11 | Multimodal DBN for Predicting High-Quality Answers in cQA portals | 25 |
| 12 | A Tag Recommendation Method for Microblog Users | 2 |
| 13 | A Forum Retrieval Model Based on Structure Mining | 0 |
| 14 | Learning to Detect Hedges and their Scope Using CRF | 5 |
| 15 | Modeling Semantic Relevance for Question-Answer Pairs in Web Social Communities | 39 |
| 16 | Mining Construction Rules of Chinese Keyphrase Based on Rough Set Theory | 3 |
| 17 | Pinyin-to-Character Conversion Model Based on Support Vector Machines | 11 |
| 18 | 3 | |
| 19 | The Effectiveness Study of Local Maximum Feature for Chinese Unknown Word Identification. | 1 |
| 20 | 2 |
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.