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
20201.4k citationsZhangyin Feng, Daya Guo et al.profile →
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
2015947 citationsDuyu Tang, Bing Qin et al.profile →
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
2014781 citationsDuyu Tang, Furu Wei et al.profile →
Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification
2014708 citationsLi Dong, Furu Wei et al.profile →
Aspect Level Sentiment Classification with Deep Memory Network
2016675 citationsDuyu Tang, Bing Qin et al.profile →
This map shows the geographic impact of Duyu Tang'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 Duyu Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Duyu Tang more than expected).
This network shows the impact of papers produced by Duyu Tang. 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 Duyu Tang. The network helps show where Duyu Tang may publish in the future.
Co-authorship network of co-authors of Duyu Tang
This figure shows the co-authorship network connecting the top 25 collaborators of Duyu Tang.
A scholar is included among the top collaborators of Duyu Tang 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 Duyu Tang. Duyu Tang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Feng, Xiaocheng, Duyu Tang, Bing Qin, & Ting Liu. (2016). English-Chinese Knowledge Base Translation with Neural Network. International Conference on Computational Linguistics. 2935–2944.7 indexed citations
17.
Tang, Duyu, Bing Qin, Xiaocheng Feng, & Ting Liu. (2016). Effective LSTMs for Target-Dependent Sentiment Classification. International Conference on Computational Linguistics. 3298–3307.249 indexed citations
18.
Tang, Duyu, et al.. (2015). User modeling with neural network for review rating prediction. International Conference on Artificial Intelligence. 1340–1346.87 indexed citations
Tang, Duyu, Furu Wei, Bing Qin, Ming Zhou, & Ting Liu. (2014). Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach. International Conference on Computational Linguistics. 172–182.122 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.