John Bear

1.7k citations
30 papers · 847 · h-index 16

Impact in

Papers in

    • Natural Language Processing Techniques 20
    • Speech and dialogue systems 10
    • Topic Modeling 5
    • Semantic Web and Ontologies 4
    • Logic, programming, and type systems 4
    • Speech Recognition and Synthesis 4
    • Logic, Reasoning, and Knowledge 3
    • Phonetics and Phonology Research 3

John Bear

30 papers receiving 683 citations

Peers

John Bear
Comparison fields: 5 of 57
  • Artificial Intelligence 774
  • Experimental and Cognitive Psychology 79
  • Information Systems 119
  • Language and Linguistics 52
  • Signal Processing 39
Replace B. K. Boguraev with:
B. K. Boguraev United States
Caroline Sporleder Germany
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Citations per field
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Citations per year

Countries citing papers authored by John Bear

Since Specialization
Citations

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

Fields of papers citing papers by John Bear

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1
FASTUS: A Finite-state Processor for Information Extraction from Real-world Text.
1993174
2 199587
3 199386
4 199277
5 199355
6 199137
7 199236
8 199333
9 199031
10 199230
11 198628
12 199024
13
Using Information Extraction to Improve Document Retrieval
199823
14 199023
15 199217
16 199316
17 198811
18 198810
19
A System for Labeling Self-Repairs in Speech
19937
20 20167

About John Bear

John Bear is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology, Information Systems, Hardware and Architecture and Computational Theory and Mathematics, having authored 30 papers that have together received 847 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (20 papers), Speech and dialogue systems (10 papers), Topic Modeling (5 papers), Semantic Web and Ontologies (4 papers), Logic, programming, and type systems (4 papers), Speech Recognition and Synthesis (4 papers), Phonetics and Phonology Research (3 papers) and Logic, Reasoning, and Knowledge (3 papers). The work is most often cited by research in Artificial Intelligence (774 citations), Experimental and Cognitive Psychology (79 citations), Information Systems (119 citations), Language and Linguistics (52 citations) and Signal Processing (39 citations). John Bear has collaborated with scholars based in United States. Frequent co-authors include Jerry R. Hobbs, Douglas E. Appelt, David Israël, Mabry Tyson, John Dowding, Elizabeth Shriberg, Robert T. Moore, Jean Mark Gawron, Lynn Cherny and Megumi Kameyama. Their work appears in journals such as Computational Linguistics, Austrian Journal of Statistics, International Joint Conference on Artificial Intelligence, Medical Entomology and Zoology and Defense Technical Information Center (DTIC).

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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