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.
Ensemble of feature sets and classification algorithms for sentiment classification
2010418 citationsRui Xia, Chengqing Zong et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Chengqing Zong
Since
Specialization
Citations
This map shows the geographic impact of Chengqing Zong'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 Chengqing Zong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chengqing Zong more than expected).
This network shows the impact of papers produced by Chengqing Zong. 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 Chengqing Zong. The network helps show where Chengqing Zong may publish in the future.
Co-authorship network of co-authors of Chengqing Zong
This figure shows the co-authorship network connecting the top 25 collaborators of Chengqing Zong.
A scholar is included among the top collaborators of Chengqing Zong 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 Chengqing Zong. Chengqing Zong is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zong, Chengqing, et al.. (2018). Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference. International Conference on Computational Linguistics. 414–425.1 indexed citations
10.
Li, Junjie, et al.. (2018). Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings.. International Conference on Computational Linguistics. 925–936.20 indexed citations
11.
Zhang, Jiajun, et al.. (2015). A new input method for human translators: integrating machine translation effectively and imperceptibly. International Conference on Artificial Intelligence. 1163–1169.10 indexed citations
12.
Xia, Rui, Chengqing Zong, Xuelei Hu, & Erik Cambria. (2015). Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification (Extended Abstract). International Joint Conference on Artificial Intelligence. 4229–4233.3 indexed citations
Xia, Rui, et al.. (2013). Dual training and dual prediction for polarity classification. Queensland's institutional digital repository (The University of Queensland).15 indexed citations
15.
Zhai, Feifei, Jiajun Zhang, Yu Zhou, & Chengqing Zong. (2012). Tree-based Translation without using Parse Trees. International Conference on Computational Linguistics. 3037–3054.6 indexed citations
16.
Wang, Zhiguo & Chengqing Zong. (2011). Parse Reranking Based on Higher-Order Lexical Dependencies. International Joint Conference on Natural Language Processing. 1251–1259.8 indexed citations
17.
Xia, Rui & Chengqing Zong. (2010). Exploring the Use of Word Relation Features for Sentiment Classification. International Conference on Computational Linguistics. 1336–1344.47 indexed citations
18.
Wang, Kun, Chengqing Zong, & Keh‐Yih Su. (2009). Which is More Suitable for Chinese Word Segmentation, the Generative Model or the Discriminative One?. Pacific Asia Conference on Language, Information, and Computation. 827–834.16 indexed citations
19.
Yu, Zhou, et al.. (2004). Multi-engine based Chinese-to-English translation system.. IWSLT. 73–77.5 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.