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.
201914.5k citationsMing‐Wei Chang, Kenton Lee et al.profile →
Natural Questions: A Benchmark for Question Answering Research
2019984 citationsKenton Lee, Ming‐Wei Chang et al.profile →
Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001
Countries citing papers authored by Ming‐Wei Chang
Since
Specialization
Citations
This map shows the geographic impact of Ming‐Wei Chang'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 Ming‐Wei Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming‐Wei Chang more than expected).
This network shows the impact of papers produced by Ming‐Wei Chang. 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 Ming‐Wei Chang. The network helps show where Ming‐Wei Chang may publish in the future.
Co-authorship network of co-authors of Ming‐Wei Chang
This figure shows the co-authorship network connecting the top 25 collaborators of Ming‐Wei Chang.
A scholar is included among the top collaborators of Ming‐Wei Chang 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 Ming‐Wei Chang. Ming‐Wei Chang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Guu, Kelvin, et al.. (2020). Retrieval Augmented Language Model Pre-Training. International Conference on Machine Learning. 1. 3929–3938.144 indexed citations
Chang, Ming‐Wei. (2016). From Entity Linking to Question Answering – Recent Progress on Semantic Grounding Tasks. International Conference on Computational Linguistics. 2.1 indexed citations
Chang, Ming‐Wei, et al.. (2012). Unified Expectation Maximization. North American Chapter of the Association for Computational Linguistics. 688–698.23 indexed citations
18.
Clarke, James, Dan Goldwasser, Ming‐Wei Chang, & Dan Roth. (2010). Driving Semantic Parsing from the World's Response. 18–27.138 indexed citations
19.
Sammons, Mark, V. G. Vinod Vydiswaran, Tim Vieira, et al.. (2009). Relation Alignment for Textual Entailment Recognition.. Theory and applications of categories.21 indexed citations
20.
Chang, Ming‐Wei, Lev Ratinov, Dan Roth, & Vivek Srikumar. (2008). Importance of semantic representation: dataless classification. National Conference on Artificial Intelligence. 830–835.145 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.