Mo Yu
- Artificial Intelligence top 0.5%
- Topic Modeling 50
- Natural Language Processing Techniques 37
- Domain Adaptation and Few-Shot Learning 9
- Speech and dialogue systems 6
- Advanced Text Analysis Techniques 6
- Advanced Graph Neural Networks 5
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- Multimodal Machine Learning Applications 19
- Advanced Image and Video Retrieval Techniques 5
- Information Systems top 2%
- Health Informatics top 10%
Mo Yu
76 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 111
- Artificial Intelligence 2.3k
- Computer Vision and Pattern Recognition 551
- Information Systems 400
- Health Informatics 18
- Management Science and Operations Research 106
Countries citing papers authored by Mo Yu
This map shows the geographic impact of Mo Yu'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 Mo Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mo Yu more than expected).
Fields of papers citing papers by Mo Yu
This network shows the impact of papers produced by Mo Yu. 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 Mo Yu. The network helps show where Mo Yu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Mo Yu, 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 | 2024 | 0 | |
| 3 | 2024 | 1 | |
| 4 | 2022 | 4 | |
| 5 | 2022 | 22 | |
| 6 | 2021 | 13 | |
| 7 | 2019 | 68 | |
| 8 | R 3 : Reinforced Ranker-Reader for Open-Domain Question Answering. | 2018 | 88 |
| 9 | 2018 | 45 | |
| 10 | 2018 | 153 | |
| 11 | 2018 | 39 | |
| 12 | A Structured Self-Attentive Sentence Embedding. | 2017 | 182 |
| 13 | Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering | 2017 | 49 |
| 14 | 2016 | 3 | |
| 15 | End-to-End Reading Comprehension with Dynamic Answer Chunk Ranking. | 2016 | 6 |
| 16 | 2014 | 4 | |
| 17 | Learning domain differences automatically for dependency parsing adaptation | 2013 | 3 |
| 18 | Cross-lingual Projections between Languages from Different Families | 2013 | 3 |
| 19 | Locally Training the Log-Linear Model for SMT | 2012 | 15 |
| 20 | Target-dependent Twitter Sentiment Classificationbreakdown → | 2011 | 578 |
About Mo Yu
Mo Yu is a scholar working on Artificial Intelligence, Computational Mathematics, Computer Vision and Pattern Recognition, General Social Sciences and Statistical and Nonlinear Physics, having authored 85 papers that have together received 2.8k indexed citations. Recurring topics across this work include Topic Modeling (50 papers), Natural Language Processing Techniques (37 papers), Multimodal Machine Learning Applications (19 papers), Domain Adaptation and Few-Shot Learning (9 papers), Speech and dialogue systems (6 papers), Advanced Text Analysis Techniques (6 papers), Advanced Graph Neural Networks (5 papers) and Advanced Image and Video Retrieval Techniques (5 papers). The work is most often cited by research in Artificial Intelligence (2.3k citations), Computer Vision and Pattern Recognition (551 citations), Information Systems (400 citations), Health Informatics (18 citations) and Management Science and Operations Research (106 citations). Mo Yu has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Tiejun Zhao, Ming Zhou, Xiaohua Liu, Long Jiang, Mark Dredze, Shiyu Chang, Bowen Zhou, Bing Xiang, Xiaoxiao Guo and Wenhan Xiong. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Proceedings of the ACM on Human-Computer Interaction, Chemical Communications, The Astrophysical Journal and Computer Methods in Applied Mechanics and 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.