Ming‐Wei Chang

47.0k citations
216 papers · 23.2k indexed · 4 hit papers · h-index 49

Ming‐Wei Chang

215 papers receiving 21.4k citations

Hit Papers

14.5k20042026201120184.0k8.0k12.0k

Peers

Ming‐Wei Chang
Comparison fields: 5 of 220
  • Artificial Intelligence 16.5k
  • Computer Vision and Pattern Recognition 4.2k
  • Pharmaceutical Science 771
  • Biomaterials 1.6k
  • Information Systems 2.4k
Replace Hao Wang with:
Hao Wang China
Haesun Park United States
Ben Niu China
Min Chen China
Suhang Wang United States
Fang Chen China
Yuan Luo United States
Mohamed Hashem Saudi Arabia
Yi Pan United States
Muhammad Ali Imran United Kingdom
Ming‐Wei Chang relative to Hao Wang China Hao Wang's profile →
Citations per field
00.5×10×15×19.8×
Hao Wang · 1×
Citations per year

Countries citing papers authored by Ming‐Wei Chang

Since Specialization
Citations

This map shows the geographic impact of Ming‐Wei Chang'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 Ming‐Wei Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming‐Wei Chang more than expected).

Fields of papers citing papers by Ming‐Wei Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming‐Wei Chang. 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 Ming‐Wei Chang. The network helps show where Ming‐Wei Chang may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Ming‐Wei Chang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ming‐Wei Chang Line = papers co-authored together Ming‐Wei Chang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20248
3 20246
4 202434
5 202320
6 202222
7 20223
8 202286
9 202119
10 202039
11
Retrieval Augmented Language Model Pre-Training
2020144
12 20188
13
Implicit ReasoNet: Modeling Large-Scale Structured Relationships with Shared Memory
20175
14 2016210
15
From Entity Linking to Question Answering – Recent Progress on Semantic Grounding Tasks
20161
16 20132
17
Unified Expectation Maximization
201223
18
Driving Semantic Parsing from the World's Response
2010138
19
Relation Alignment for Textual Entailment Recognition.
200921
20
Importance of semantic representation: dataless classification
2008145

About Ming‐Wei Chang

Ming‐Wei Chang is a scholar working on Biomaterials, Pharmaceutical Science, Artificial Intelligence, Surfaces, Coatings and Films and Biomedical Engineering, having authored 216 papers that have together received 23.2k indexed citations. Recurring topics across this work include Topic Modeling (49 papers), Electrospun Nanofibers in Biomedical Applications (49 papers), Natural Language Processing Techniques (45 papers), Electrohydrodynamics and Fluid Dynamics (43 papers), Advanced Sensor and Energy Harvesting Materials (18 papers), Advanced Drug Delivery Systems (14 papers), Multimodal Machine Learning Applications (13 papers) and Advancements in Transdermal Drug Delivery (13 papers). The work is most often cited by research in Artificial Intelligence (16.5k citations), Computer Vision and Pattern Recognition (4.2k citations), Pharmaceutical Science (771 citations), Biomaterials (1.6k citations) and Information Systems (2.4k citations). Ming‐Wei Chang has collaborated with scholars based in United Kingdom, China and United States. Frequent co-authors include Kenton Lee, Kristina Toutanova, Jacob Devlin, Zeeshan Ahmad, Wen-tau Yih, Jingsong Li, Chih‐Jen Lin, Bing Chen, Dan Roth and Xiaodong He. Their work appears in journals such as Chemical Engineering Journal, Journal of Drug Delivery Science and Technology, Pharmaceutics, RSC Advances and Materials Letters.

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.

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