David L. Chen

762 citations
6 papers · 422 indexed · h-index 5
Topics
Natural Language Processing Techniques (3 papers)Software Engineering Research (3 papers)Topic Modeling (3 papers)
Journals
ACM SIGPLAN NoticesProceedings of the AAAI Conference on Artificial Intelligence
Partner nations
United States

In The Last Decade

David L. Chen

5 papers receiving 396 citations

Peers

David L. Chen
Comparison fields: 5 of 38
  • Artificial Intelligence 359
  • Computer Vision and Pattern Recognition 176
  • Computer Networks and Communications 46
  • Information Systems 42
  • Software 22
Replace Jaime Carbonell with:
Jaime Carbonell United States
Boxing Chen China
Quentin Anthony United States
Horace He United States
Yingke Chen China
Torbjörn Lager Sweden
Raghav Gupta United States
Leon Barrett United States
Norihito Yasuda Japan
Corin Gurr United Kingdom
David L. Chen relative to Jaime Carbonell United States Jaime Carbonell's profile →
Citations per field
00.5×3.9×
Jaime Carbonell · 1×
Citations per year

Countries citing papers authored by David L. Chen

Since Specialization
Citations

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

Fields of papers citing papers by David L. Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David L. Chen. 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 David L. Chen. The network helps show where David L. Chen may publish in the future.

Co-authorship network of co-authors of David L. Chen

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

All Works

6 of 6 papers shown
#WorkIndexed citations
1 0
2 232
3
Panning for Gold: Finding Relevant Semantic Content for Grounded Language Learning
4
4 130
5 9
6 47

About David L. Chen

David L. Chen is a scholar working on Software, Hardware and Architecture and Information Systems, having authored 6 papers that have together received 422 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (3 papers), Software Engineering Research (3 papers) and Topic Modeling (3 papers). The work is most often cited by research in Artificial Intelligence (359 citations), Computer Vision and Pattern Recognition (176 citations) and Software (22 citations). David L. Chen has collaborated with scholars based in United States. Frequent co-authors include Raymond J. Mooney, Donald E. Porter, Indrajit Roy, Jung-Woo Ha, Christopher J. Rossbach, Jason V. Davis, Emmett Witchel and Hany E. Ramadan. Their work appears in journals such as ACM SIGPLAN Notices and Proceedings of the AAAI Conference on Artificial Intelligence.

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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