David Day

845 citations
27 papers · 518 · h-index 11

Impact in

    • Natural Language Processing Techniques
    • Topic Modeling
    • Semantic Web and Ontologies
    • Speech and dialogue systems
    • Advanced Text Analysis Techniques
    • AI-based Problem Solving and Planning
    • Web Data Mining and Analysis

Papers in

    • Natural Language Processing Techniques 11
    • Topic Modeling 10
    • Semantic Web and Ontologies 9
    • AI-based Problem Solving and Planning 3
    • Speech and dialogue systems 2
    • Logic, Reasoning, and Knowledge 2
    • Biomedical Text Mining and Ontologies 4

David Day

25 papers receiving 424 citations

Peers

David Day
Comparison fields: 5 of 63
  • Artificial Intelligence 439
  • Information Systems 96
  • Management Science and Operations Research 35
  • Signal Processing 21
  • Geography, Planning and Development 9
Replace Kevin Humphreys with:
Kevin Humphreys United Kingdom
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Citations per field
00.5×1.5×
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Citations per year

Countries citing papers authored by David Day

Since Specialization
Citations

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

Fields of papers citing papers by David Day

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside David Day, 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 David Day Line = papers co-authored together David Day links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1997102
2 199585
3 200056
4 199748
5
Recognizing and Organizing Opinions Expressed in the World Press
200344
6 198734
7 199632
8 198524
9 200421
10 200012
11 200210
12
Real users, real data, real problems: the MiTAP system for monitoring bio events
20028
13 19968
14 20086
15 19806
16 20025
17 20023
18 19883
19 20223
20 20012

About David Day

David Day is a scholar working on Artificial Intelligence, Molecular Biology, Sociology and Political Science, Information Systems and Management Science and Operations Research, having authored 27 papers that have together received 518 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (11 papers), Topic Modeling (10 papers), Semantic Web and Ontologies (9 papers), Biomedical Text Mining and Ontologies (4 papers), AI-based Problem Solving and Planning (3 papers), Speech and dialogue systems (2 papers), Web Data Mining and Analysis (2 papers) and Logic, Reasoning, and Knowledge (2 papers). The work is most often cited by research in Artificial Intelligence (439 citations), Information Systems (96 citations), Management Science and Operations Research (35 citations), Signal Processing (21 citations) and Geography, Planning and Development (9 citations). David Day has collaborated with scholars based in United States, Italy and Egypt. Frequent co-authors include Marc Vilain, Lynette Hirschman, John Aberdeen, Patricia Robinson, David D. Palmer, Robyn Kozierok, John D. Burger, Paul R. Cohen, John C. Henderson and Steven Bird. Their work appears in journals such as Language Resources and Evaluation, International Journal of Approximate Reasoning, ˜The œJohn Marshall law review, National Conference on Artificial Intelligence and Scholarworks (University of Massachusetts Amherst).

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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