Mo Yu

9.5k total citations · 1 hit paper
85 papers, 2.8k citations indexed

About

Mo Yu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Mo Yu has authored 85 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Artificial Intelligence, 24 papers in Computer Vision and Pattern Recognition and 9 papers in Information Systems. Recurrent topics in Mo Yu's work include Topic Modeling (50 papers), Natural Language Processing Techniques (37 papers) and Multimodal Machine Learning Applications (19 papers). Mo Yu is often cited by papers focused on Topic Modeling (50 papers), Natural Language Processing Techniques (37 papers) and Multimodal Machine Learning Applications (19 papers). Mo Yu collaborates with scholars based in United States, China and Canada. Mo Yu's co-authors include Tiejun Zhao, Xiaohua Liu, Ming Zhou, Long Jiang, Mark Dredze, Shiyu Chang, Bowen Zhou, Bing Xiang, Xiaoxiao Guo and Wenhan Xiong and has published in prestigious journals such as The Astrophysical Journal, Chemical Communications and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Mo Yu

76 papers receiving 2.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Target-dependent Twitter Sentiment Classification

2011 578 citations Long Jiang, Mo Yu et al. Meeting of the Association for Computational Linguistics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mo Yu United States	26	2.3k	551	400	136	106	85	2.8k
Jean Y. Wu United States	5	3.4k 1.5×	477 0.9×	431 1.1×	208 1.5×	127 1.2×	6	3.8k
Minghui Qiu China	23	1.5k 0.6×	501 0.9×	676 1.7×	142 1.0×	78 0.7×	85	2.1k
Yinfei Yang United States	21	1.8k 0.8×	757 1.4×	318 0.8×	194 1.4×	58 0.5×	37	2.5k
Lidong Bing China	31	4.5k 2.0×	541 1.0×	767 1.9×	151 1.1×	208 2.0×	154	5.1k
Ivan Titov United Kingdom	30	3.4k 1.5×	887 1.6×	442 1.1×	139 1.0×	122 1.2×	106	4.5k
Tianyu Gao China	13	2.9k 1.2×	759 1.4×	397 1.0×	79 0.6×	147 1.4×	19	3.3k
Kevin Gimpel United States	22	2.4k 1.0×	395 0.7×	302 0.8×	124 0.9×	81 0.8×	81	2.8k
Jinlan Fu China	15	2.1k 0.9×	538 1.0×	439 1.1×	81 0.6×	193 1.8×	28	2.9k
Trevor Cohn Australia	33	3.1k 1.4×	564 1.0×	366 0.9×	302 2.2×	70 0.7×	186	3.8k

Countries citing papers authored by Mo Yu

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Mo Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Mo Yu. A scholar is included among the top collaborators of Mo Yu 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 Mo Yu. Mo Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Zhang, Tingting, et al.. (2025). Cu-catalyzed borylative cyclization of N-(o-alkynylaryl)imines. Chemical Communications. 61(37). 6799–6802.

2.

Meng, Fandong, et al.. (2024). On Large Language Models’ Hallucination with Regard to Known Facts. 1041–1053.

3.

Yu, Mo, et al.. (2023). Personality Understanding of Fictional Characters during Book Reading. 14784–14802. 2 indexed citations

4.

Zhang, Xingyu, et al.. (2022). Probing Script Knowledge from Pre-Trained Models. 87–93. 4 indexed citations

5.

Wang, Sijia, Mo Yu, Shiyu Chang, Lichao Sun, & Lifu Huang. (2022). Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding. Findings of the Association for Computational Linguistics: ACL 2022. 169–182. 22 indexed citations

6.

Li, Manling, Tengfei Ma, Mo Yu, et al.. (2021). Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 6443–6456. 13 indexed citations

7.

Yu, Mo, Shiyu Chang, Yang Zhang, & Tommi Jaakkola. (2019). Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control. 4092–4101. 68 indexed citations

8.

Wang, Shuohang, Mo Yu, Xiaoxiao Guo, et al.. (2018). R 3 : Reinforced Ranker-Reader for Open-Domain Question Answering.. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 5981–5988. 88 indexed citations

9.

Bao, Yujia, Shiyu Chang, Mo Yu, & Regina Barzilay. (2018). Deriving Machine Attention from Human Rationales. 1903–1913. 45 indexed citations

10.

Xiong, Wenhan, Mo Yu, Shiyu Chang, Xiaoxiao Guo, & William Yang Wang. (2018). One-Shot Relational Learning for Knowledge Graphs. 1980–1990. 153 indexed citations

11.

Xu, Kun, Lingfei Wu, Zhiguo Wang, et al.. (2018). Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model. 918–924. 39 indexed citations

12.

Wang, Shuohang, Mo Yu, Jing Jiang, et al.. (2017). Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering. Singapore Management University Institutional Knowledge (InK) (Singapore Management University). 1. 49 indexed citations

13.

Yu, Mo, Xiaolong Zhang, & Derek A. Kreager. (2016). New to online dating?: learning from experienced users for a successful match. 467–470. 3 indexed citations

14.

Yu, Yang, Wei Zhang, Kazi Saidul Hasan, et al.. (2016). End-to-End Reading Comprehension with Dynamic Answer Chunk Ranking.. arXiv (Cornell University). 6 indexed citations

15.

Yu, Mo, et al.. (2014). Who were you talking to: mining interpersonal relationships from cellphone network data. 485–490. 4 indexed citations

16.

Yu, Mo, Tiejun Zhao, Daxiang Dong, Hao Tian, & Dianhai Yu. (2013). Compound Embedding Features for Semi-supervised Learning. North American Chapter of the Association for Computational Linguistics. 563–568. 17 indexed citations

17.

Yu, Mo, Tiejun Zhao, Yalong Bai, Hao Tian, & Dianhai Yu. (2013). Cross-lingual Projections between Languages from Different Families. Meeting of the Association for Computational Linguistics. 2. 312–317. 3 indexed citations

18.

Yu, Mo, Tiejun Zhao, & Yalong Bai. (2013). Learning domain differences automatically for dependency parsing adaptation. International Joint Conference on Artificial Intelligence. 1876–1882. 3 indexed citations

19.

Liu, Lemao, et al.. (2012). Locally Training the Log-Linear Model for SMT. Empirical Methods in Natural Language Processing. 402–411. 15 indexed citations

20.

Jiang, Long, Mo Yu, Ming Zhou, Xiaohua Liu, & Tiejun Zhao. (2011). Target-dependent Twitter Sentiment Classification. Meeting of the Association for Computational Linguistics. 151–160. 578 indexed citations breakdown →

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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