Lung‐Hao Lee
- Artificial Intelligence top 2%
- Information Systems top 10%
- Computer Vision and Pattern Recognition top 10%
- Cognitive Neuroscience
- Signal Processing
- Co-authors
- Hsin‐Hsi ChenYuen‐Hsien TsengLiping ChangLiang YuChao-Lin LiuShih-Hung WuPo‐Lei LeeKuo‐Kai Shyu
- Topics
- Topic Modeling (31 papers)Natural Language Processing Techniques (27 papers)Spam and Phishing Detection (10 papers)
- Partner nations
- TaiwanHong KongUnited States
In The Last Decade
Lung‐Hao Lee
54 papers receiving 592 citations
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 508
- Information Systems 99
- Computer Vision and Pattern Recognition 83
- Cognitive Neuroscience 48
- Signal Processing 36
Countries citing papers authored by Lung‐Hao Lee
This map shows the geographic impact of Lung‐Hao Lee'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 Lung‐Hao Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lung‐Hao Lee more than expected).
Fields of papers citing papers by Lung‐Hao Lee
This network shows the impact of papers produced by Lung‐Hao Lee. 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 Lung‐Hao Lee. The network helps show where Lung‐Hao Lee may publish in the future.
Co-authorship network of co-authors of Lung‐Hao Lee
This figure shows the co-authorship network connecting the top 25 collaborators of Lung‐Hao Lee. A scholar is included among the top collaborators of Lung‐Hao Lee 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 Lung‐Hao Lee. Lung‐Hao Lee 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 | 2 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 13 | |
| 7 | 1 | |
| 8 | NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis. | 1 |
| 9 | 12 | |
| 10 | IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases | 2 |
| 11 | IJCNLP-2017 Task 1: Chinese Grammatical Error Diagnosis | 14 |
| 12 | 3 | |
| 13 | 3 | |
| 14 | Overview of NLP-TEA 2016 Shared Task for Chinese Grammatical Error Diagnosis | 17 |
| 15 | A Sentence Judgment System for Grammatical Error Detection | 25 |
| 16 | 25 | |
| 17 | Chinese Spelling Check Evaluation at SIGHAN Bake-off 2013 | 73 |
| 18 | 1 | |
| 19 | 6 | |
| 20 | Chinese WordNet Domains: Bootstrapping Chinese WordNet with Semantic Domain Labels | 1 |
About Lung‐Hao Lee
Lung‐Hao Lee is a scholar working on Artificial Intelligence, Health Informatics and Information Systems, having authored 55 papers that have together received 637 indexed citations. Recurring topics across this work include Topic Modeling (31 papers), Natural Language Processing Techniques (27 papers) and Spam and Phishing Detection (10 papers). The work is most often cited by research in Artificial Intelligence (508 citations), Information Systems (99 citations) and Computer Vision and Pattern Recognition (83 citations). Lung‐Hao Lee has collaborated with scholars based in Taiwan, Hong Kong and United States. Frequent co-authors include Hsin‐Hsi Chen, Yuen‐Hsien Tseng, Liping Chang, Liang Yu, Chao-Lin Liu, Shih-Hung Wu, Po‐Lei Lee, Kuo‐Kai Shyu, Chu‐Ren Huang and Liang-Chih Yu. Their work appears in journals such as IEEE Access, Knowledge-Based Systems and IEEE Sensors Journal.
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.