Yu‐Chieh Wu
- Artificial Intelligence top 10%
- Topic Modeling 19
- Natural Language Processing Techniques 18
- Text and Document Classification Technologies 14
- Advanced Text Analysis Techniques 10
- Algorithms and Data Compression 4
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- Video Analysis and Summarization 6
- Information Systems top 10%
- Web Data Mining and Analysis 6
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- Ubiquitin and proteasome pathways 5
- Co-authors
- Jie Chi YangYue‐Shi LeeChengkai DaiVan G. WilsonLixin MiNanqin GanMathilde BrunetCarmen Garrido
- Journals
- Journal of Biological Chemistry (1 paper)PLoS ONE (1 paper)Expert Systems with Applications (1 paper)
- Partner nations
- TaiwanUnited StatesChina
In The Last Decade
Yu‐Chieh Wu
53 papers receiving 529 citations
Peers
Comparison fields: 5 of 117
- Artificial Intelligence 201
- Computer Vision and Pattern Recognition 97
- Endocrinology 20
- Information Systems 80
- Aging 6
Countries citing papers authored by Yu‐Chieh Wu
This map shows the geographic impact of Yu‐Chieh Wu'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 Yu‐Chieh Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu‐Chieh Wu more than expected).
Fields of papers citing papers by Yu‐Chieh Wu
This network shows the impact of papers produced by Yu‐Chieh Wu. 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 Yu‐Chieh Wu. The network helps show where Yu‐Chieh Wu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yu‐Chieh Wu, 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 | 2024 | 1 | |
| 2 | 2022 | 4 | |
| 3 | 2022 | 3 | |
| 4 | 2021 | 7 | |
| 5 | 2021 | 6 | |
| 6 | 2020 | 11 | |
| 7 | 2018 | 1 | |
| 8 | 2017 | 29 | |
| 9 | Combining Multiple Lexical Resources for Chinese Textual Entailment Recognition. | 2013 | 1 |
| 10 | MCU at NTCIR: A Resources Limited Chinese Textual Entailment Recognition System | 2011 | 1 |
| 11 | 2011 | 0 | |
| 12 | 2011 | 8 | |
| 13 | 2010 | 35 | |
| 14 | 2010 | 102 | |
| 15 | 2009 | 17 | |
| 16 | Description of the NCU Chinese Word Segmentation and Part-of-Speech Tagging for SIGHAN Bakeoff 2007 | 2008 | 6 |
| 17 | Tornado in Multilingual Opinion Analysis: A Transductive Learning Approach for Chinese Sentimental Polarity Recognition | 2008 | 4 |
| 18 | 2008 | 30 | |
| 19 | Multilingual Deterministic Dependency Parsing Framework using Modified Finite Newton Method Support Vector Machines | 2007 | 7 |
| 20 | A General and Multi-lingual Phrase Chunking Model based on Masking Method | 2006 | 7 |
About Yu‐Chieh Wu
Yu‐Chieh Wu is a scholar working on Artificial Intelligence, Health Informatics and Computer Vision and Pattern Recognition, having authored 56 papers that have together received 574 indexed citations. Recurring topics across this work include Topic Modeling (19 papers), Natural Language Processing Techniques (18 papers), Text and Document Classification Technologies (14 papers), Advanced Text Analysis Techniques (10 papers), Video Analysis and Summarization (6 papers), Web Data Mining and Analysis (6 papers), Ubiquitin and proteasome pathways (5 papers) and Algorithms and Data Compression (4 papers). The work is most often cited by research in Artificial Intelligence (201 citations), Computer Vision and Pattern Recognition (97 citations) and Endocrinology (20 citations). Yu‐Chieh Wu has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Jie Chi Yang, Yue‐Shi Lee, Chengkai Dai, Van G. Wilson, Lixin Mi, Nanqin Gan, Mathilde Brunet, Carmen Garrido, Fung‐Lung Chung and Show‐Jane Yen. Their work appears in journals such as Journal of Biological Chemistry, PLoS ONE and Expert Systems with Applications.
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.