Linjun Shou

4.9k total citations · 1 hit paper
39 papers, 2.0k citations indexed

About

Linjun Shou is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Linjun Shou has authored 39 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 19 papers in Computer Vision and Pattern Recognition and 6 papers in Information Systems. Recurrent topics in Linjun Shou's work include Topic Modeling (33 papers), Natural Language Processing Techniques (29 papers) and Multimodal Machine Learning Applications (17 papers). Linjun Shou is often cited by papers focused on Topic Modeling (33 papers), Natural Language Processing Techniques (29 papers) and Multimodal Machine Learning Applications (17 papers). Linjun Shou collaborates with scholars based in China, United States and Canada. Linjun Shou's co-authors include Ming Gong, Daxin Jiang, Nan Duan, Ming Zhou, Duyu Tang, Daya Guo, Zhangyin Feng, Xiaocheng Feng, Ting Liu and Bing Qin and has published in prestigious journals such as SHILAP Revista de lepidopterología, Knowledge-Based Systems and ACM Transactions on Asian and Low-Resource Language Information Processing.

In The Last Decade

Linjun Shou

37 papers receiving 2.0k 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.

CodeBERT: A Pre-Trained Model for Programming and Natural Languages

2020 1.4k citations Zhangyin Feng, Daya Guo et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Linjun Shou China	14	1.2k	1.1k	521	351	269	39	2.0k
Daya Guo China	11	1.3k 1.1×	1.6k 1.4×	766 1.5×	483 1.4×	380 1.4×	21	2.4k
Ge Li China	25	1.0k 0.8×	1.6k 1.4×	819 1.6×	532 1.5×	406 1.5×	108	2.2k
Hridesh Rajan United States	21	998 0.8×	1.3k 1.2×	512 1.0×	197 0.6×	628 2.3×	140	1.9k
Benoît Baudry France	27	1.0k 0.8×	1.4k 1.3×	1.5k 3.0×	237 0.7×	544 2.0×	126	2.3k
Guozhu Meng China	21	439 0.4×	844 0.8×	601 1.2×	833 2.4×	556 2.1×	48	1.6k
Michael L. Collard United States	20	495 0.4×	1.4k 1.2×	762 1.5×	159 0.5×	392 1.5×	60	1.5k
Spiros Mancoridis United States	23	1.3k 1.0×	2.1k 1.9×	976 1.9×	332 0.9×	985 3.7×	83	2.5k
Tim Leek United States	13	737 0.6×	680 0.6×	537 1.0×	655 1.9×	280 1.0×	17	1.4k
Bruce W. Weide United States	19	747 0.6×	743 0.7×	243 0.5×	232 0.7×	305 1.1×	90	1.6k
Xiaoyin Wang United States	24	533 0.4×	1.8k 1.6×	1.1k 2.2×	755 2.2×	729 2.7×	107	2.3k

Countries citing papers authored by Linjun Shou

Since Specialization

Citations

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

Fields of papers citing papers by Linjun Shou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Linjun Shou. 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 Linjun Shou. The network helps show where Linjun Shou may publish in the future.

Co-authorship network of co-authors of Linjun Shou

This figure shows the co-authorship network connecting the top 25 collaborators of Linjun Shou. A scholar is included among the top collaborators of Linjun Shou 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 Linjun Shou. Linjun Shou 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.

Wu, Ningxiang, et al.. (2024). ControlMath: Controllable Data Generation Promotes Math Generalist Models. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 12201–12217. 1 indexed citations

2.

Gong, Ming, et al.. (2023). Instructed Language Models with Retrievers Are Powerful Entity Linkers. 2267–2282. 2 indexed citations

3.

Chen, Nuo, Linjun Shou, Ming Gong, et al.. (2023). Structural Contrastive Pretraining for Cross-Lingual Comprehension. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 2042–2057. 1 indexed citations

4.

Shao, Bo, et al.. (2023). WIERT: Web Information Extraction via Render Tree. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 13166–13173. 3 indexed citations

5.

Chen, Nuo, Linjun Shou, Jian Pei, et al.. (2023). Alleviating Over-smoothing for Unsupervised Sentence Representation. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 3552–3566. 3 indexed citations

6.

Zhuang, Shengyao, Linjun Shou, & Guido Zuccon. (2023). Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval. arXiv (Cornell University). 1827–1832. 4 indexed citations

7.

Shou, Linjun, et al.. (2022). Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval. 3107–3121. 4 indexed citations

8.

Shou, Linjun, et al.. (2022). Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding. 9903–9918. 5 indexed citations

9.

Lian, Jianxun, Wayne Xin Zhao, Ming Gong, et al.. (2022). Negative Sampling for Contrastive Representation Learning: A Review. arXiv (Cornell University). 17 indexed citations

10.

Chen, Nuo, Linjun Shou, Ming Gong, Jian Pei, & Daxin Jiang. (2022). Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1909–1923. 4 indexed citations

11.

Eskimez, Şefik Emre, Yu Shi, Ming Gong, et al.. (2022). Improving Readability for Automatic Speech Recognition Transcription. ACM Transactions on Asian and Low-Resource Language Information Processing. 22(5). 1–23. 18 indexed citations

12.

Guo, Daya, Duyu Tang, Qinliang Su, et al.. (2021). Syntax-Enhanced Pre-trained Model. 5412–5422. 20 indexed citations

13.

Huang, Junjie, Duyu Tang, Linjun Shou, et al.. (2021). CoSQA: 20,000+ Web Queries for Code Search and Question Answering. 5690–5700. 43 indexed citations

14.

Guo, Yingmei, Linjun Shou, Jian Pei, et al.. (2021). Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 3226–3237. 1 indexed citations

15.

Liu, Yang, Chenguang Zhu, Linjun Shou, et al.. (2021). Retrieval Enhanced Model for Commonsense Generation. 3056–3062. 17 indexed citations

16.

Liu, Dayiheng, Yu Yan, Yeyun Gong, et al.. (2021). GLGE: A New General Language Generation Evaluation Benchmark. 408–420. 32 indexed citations

17.

Luo, Huaishao, Yu Shi, Ming Gong, Linjun Shou, & Tianrui Li. (2020). MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension. 687–695. 3 indexed citations

18.

Zhong, Wanjun, Duyu Tang, Zhangyin Feng, et al.. (2020). LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network. 6053–6065. 26 indexed citations

19.

Wang, Xuguang, Linjun Shou, Ming Gong, Nan Duan, & Daxin Jiang. (2020). No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension. 4141–4150. 4 indexed citations

20.

Huang, Haoyang, Yaobo Liang, Nan Duan, et al.. (2019). Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks. 2485–2494. 104 indexed citations

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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