Zhenghua Li
- Artificial Intelligence top 1%
- Topic Modeling 51
- Natural Language Processing Techniques 50
- Text Readability and Simplification 12
- Advanced Text Analysis Techniques 6
- Speech and dialogue systems 4
- Semantic Web and Ontologies 4
- Sentiment Analysis and Opinion Mining 3
- Information Systems top 5%
-
- Multimodal Machine Learning Applications 6
- Co-authors
- Wanxiang CheMin ZhangTing LiuWenliang ChenJunjie YuZhengqiu HeYu ZhangRui Wang
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Zhenghua Li
59 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 897
- Information Systems 158
- Computer Vision and Pattern Recognition 113
- Management Science and Operations Research 44
- Information Systems and Management 17
Countries citing papers authored by Zhenghua Li
This map shows the geographic impact of Zhenghua Li'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 Zhenghua Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhenghua Li more than expected).
Fields of papers citing papers by Zhenghua Li
This network shows the impact of papers produced by Zhenghua Li. 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 Zhenghua Li. The network helps show where Zhenghua Li may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Zhenghua Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 3 | |
| 5 | 2023 | 2 | |
| 6 | 2022 | 5 | |
| 7 | 2020 | 16 | |
| 8 | 2019 | 23 | |
| 9 | Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning | 2018 | 62 |
| 10 | Dependency Parsing with Partial Annotations: An Empirical Comparison | 2017 | 4 |
| 11 | Soft Cross-lingual Syntax Projection for Dependency Parsing | 2014 | 12 |
| 12 | Stacking Heterogeneous Joint Models of Chinese POS Tagging and Dependency Parsing | 2012 | 2 |
| 13 | Exploiting Multiple Treebanks for Parsing with Quasi-synchronous Grammars | 2012 | 15 |
| 14 | A Separately Passive-Aggressive Training Algorithm for Joint POS Tagging and Dependency Parsing | 2012 | 16 |
| 15 | Improving Chinese POS Tagging with Dependency Parsing | 2011 | 4 |
| 16 | Joint Models for Chinese POS Tagging and Dependency Parsing | 2011 | 50 |
| 17 | Language Technology Platform | 2011 | 19 |
| 18 | LTP: A Chinese Language Technology Platform | 2010 | 229 |
| 19 | A Study on Constituent-to-Dependency Conversion | 2008 | 0 |
| 20 | Algorithm and implementation in Chinese Character's Order of Strokes Recognition | 2004 | 1 |
About Zhenghua Li
Zhenghua Li is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 65 papers that have together received 1.1k indexed citations. Recurring topics across this work include Topic Modeling (51 papers), Natural Language Processing Techniques (50 papers), Text Readability and Simplification (12 papers), Advanced Text Analysis Techniques (6 papers), Multimodal Machine Learning Applications (6 papers), Speech and dialogue systems (4 papers), Semantic Web and Ontologies (4 papers) and Sentiment Analysis and Opinion Mining (3 papers). The work is most often cited by research in Artificial Intelligence (897 citations), Information Systems (158 citations) and Computer Vision and Pattern Recognition (113 citations). Zhenghua Li has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Wanxiang Che, Min Zhang, Ting Liu, Wenliang Chen, Junjie Yu, Wenliang Chen, Zhengqiu He, Yu Zhang, Ting Liu and Rui Wang.
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.