Pengfei Liu
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 5%
- Signal Processing top 5%
- Molecular Biology
- Co-authors
- Xipeng QiuXuanjing HuangHelen MengShafiq JotyXinchi ChenGraham NeubigYixin LiuDragomir Radev
- Topics
- Topic Modeling (41 papers)Natural Language Processing Techniques (34 papers)Advanced Text Analysis Techniques (17 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Pengfei Liu
73 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 119
- Artificial Intelligence 2.0k
- Computer Vision and Pattern Recognition 316
- Information Systems 236
- Signal Processing 132
- Molecular Biology 74
Countries citing papers authored by Pengfei Liu
This map shows the geographic impact of Pengfei 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 Pengfei Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pengfei Liu more than expected).
Fields of papers citing papers by Pengfei Liu
This network shows the impact of papers produced by Pengfei 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 Pengfei Liu. The network helps show where Pengfei Liu may publish in the future.
Co-authorship network of co-authors of Pengfei Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Pengfei Liu. A scholar is included among the top collaborators of Pengfei 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 Pengfei Liu. Pengfei 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 | 0 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 4 | |
| 6 | BRIO: Bringing Order to Abstractive Summarizationbreakdown → | 120 |
| 7 | 0 | |
| 8 | 60 | |
| 9 | 113 | |
| 10 | 102 | |
| 11 | Task assignment method based on cloud-fog cooperative model | 0 |
| 12 | Adversarial Multi-task Learning for Text Classificationbreakdown → | 393 |
| 13 | 59 | |
| 14 | Recurrent neural network for text classification with multi-task learning | 116 |
| 15 | 152 | |
| 16 | Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddingsbreakdown → | 290 |
| 17 | 103 | |
| 18 | Learning context-sensitive word embeddings with neural tensor skip-gram model | 51 |
| 19 | 11 | |
| 20 | An empirical study on college students physical exercise behavior change based on the transtheoretical model | 1 |
About Pengfei Liu
Pengfei Liu is a scholar working on Artificial Intelligence, Information Systems and General Social Sciences, having authored 80 papers that have together received 2.3k indexed citations. Recurring topics across this work include Topic Modeling (41 papers), Natural Language Processing Techniques (34 papers) and Advanced Text Analysis Techniques (17 papers). The work is most often cited by research in Artificial Intelligence (2.0k citations), Computer Vision and Pattern Recognition (316 citations) and Health Informatics (17 citations). Pengfei Liu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xipeng Qiu, Xuanjing Huang, Helen Meng, Shafiq Joty, Xinchi Chen, Graham Neubig, Yixin Liu, Dragomir Radev, Jifan Chen and Chenxi Zhu. Their work appears in journals such as Bioinformatics, Sensors and Ocean 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.