Xueqiang Lv
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Information Systems
- Computer Networks and Communications
- Media Technology
- Topics
- Natural Language Processing Techniques (13 papers)Topic Modeling (9 papers)Advanced Text Analysis Techniques (7 papers)
- Journals
- Expert Systems with ApplicationsIEEE Transactions on CyberneticsFuture Generation Computer Systems
- Partner nations
- ChinaUnited KingdomTaiwan
In The Last Decade
Xueqiang Lv
31 papers receiving 207 citations
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 125
- Computer Vision and Pattern Recognition 59
- Information Systems 36
- Computer Networks and Communications 16
- Media Technology 15
Countries citing papers authored by Xueqiang Lv
This map shows the geographic impact of Xueqiang Lv'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 Xueqiang Lv with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xueqiang Lv more than expected).
Fields of papers citing papers by Xueqiang Lv
This network shows the impact of papers produced by Xueqiang Lv. 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 Xueqiang Lv. The network helps show where Xueqiang Lv may publish in the future.
Co-authorship network of co-authors of Xueqiang Lv
This figure shows the co-authorship network connecting the top 25 collaborators of Xueqiang Lv. A scholar is included among the top collaborators of Xueqiang Lv 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 Xueqiang Lv. Xueqiang Lv 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 | 3 | |
| 3 | 2 | |
| 4 | 6 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 2 | |
| 9 | 1 | |
| 10 | 0 | |
| 11 | 21 | |
| 12 | 1 | |
| 13 | 3 | |
| 14 | 2 | |
| 15 | Chinese Lexical Semantic Similarity Computing Based on Large-scale Corpus | 1 |
| 16 | IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence | 53 |
| 17 | Semantic v.s. Positions: Utilizing Balanced Proximity in Language Model Smoothing for Information Retrieval | 3 |
| 18 | 0 | |
| 19 | Unsupervised Chinese Personal Name Recognition Using Search Session | 0 |
| 20 | Towards Chinese-English Sentence Alignment Based on Statistical Method | 1 |
About Xueqiang Lv
Xueqiang Lv is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 38 papers that have together received 219 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (13 papers), Topic Modeling (9 papers) and Advanced Text Analysis Techniques (7 papers). The work is most often cited by research in Artificial Intelligence (125 citations), Computer Vision and Pattern Recognition (59 citations) and Media Technology (15 citations). Xueqiang Lv has collaborated with scholars based in China, United Kingdom and Taiwan. Frequent co-authors include Shou-De Lin, Han Jiang, Mirella Lapata, Rui Yan, Xiaoming Li, Xiaoming Li, Yue Huang, Hailong Liu, Baoan Li and Kai Zhang. Their work appears in journals such as Expert Systems with Applications, IEEE Transactions on Cybernetics and Future Generation Computer Systems.
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.