Jonathan May

2.9k citations
92 papers · 1.5k · h-index 21

Impact in

Papers in

    • Natural Language Processing Techniques 59
    • Topic Modeling 59
    • Text Readability and Simplification 8
    • Speech and dialogue systems 7
    • Speech Recognition and Synthesis 6
    • Hate Speech and Cyberbullying Detection 5
    • Multimodal Machine Learning Applications 11

Jonathan May

79 papers receiving 1.4k citations

Peers

Jonathan May
Comparison fields: 5 of 90
  • Artificial Intelligence 1.3k
  • Computer Vision and Pattern Recognition 238
  • Computational Theory and Mathematics 159
  • Management Science and Operations Research 60
  • Information Systems 99
Replace Degen Huang with:
Degen Huang China
Suge Wang China
Hiroya Takamura Japan
Diarmuid Ó Séaghdha United Kingdom
Yi Fang United States
Cícero dos Santos Brazil
Makoto Iwayama Japan
Julian Kupiec United States
Shourya Roy India
Jianglei Han Singapore
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Citations per field
00.5×2.6×
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Citations per year

Countries citing papers authored by Jonathan May

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan May

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jonathan May, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jonathan May Line = papers co-authored together Jonathan May links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 92 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017219
2
Tuning as Ranking
2011186
3 202184
4 201875
5 202050
6 201948
7 200648
8 201541
9 201741
10 201035
11 201635
12
TREC 2002 QA at BBN: Answer Selection and Confidence Estimation.
200234
13 202333
14 200933
15
Syntactic Re-Alignment Models for Machine Translation
200729
16 200929
17 200625
18 201422
19 201922
20 200722

About Jonathan May

Jonathan May is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Information Systems and Computer Networks and Communications, having authored 92 papers that have together received 1.5k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (59 papers), Topic Modeling (59 papers), Multimodal Machine Learning Applications (11 papers), Text Readability and Simplification (8 papers), semigroups and automata theory (8 papers), Speech and dialogue systems (7 papers), Speech Recognition and Synthesis (6 papers) and Hate Speech and Cyberbullying Detection (5 papers). The work is most often cited by research in Artificial Intelligence (1.3k citations), Computer Vision and Pattern Recognition (238 citations), Computational Theory and Mathematics (159 citations), Management Science and Operations Research (60 citations) and Information Systems (99 citations). Jonathan May has collaborated with scholars based in United States, Germany and Sweden. Frequent co-authors include Kevin Knight, Mark Hopkins, Heng Ji, Xiaoman Pan, Boliang Zhang, Joel Nothman, Xiang Ren, Mozhdeh Gheini, Nanyun Peng and Andreas Maletti. Their work appears in journals such as Language Resources and Evaluation, Big Data, Biotechnology and Bioengineering, Theoretical Computer Science and Machine Translation.

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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