Tim O’Gorman

635 citations
27 papers · 357 · h-index 11

Impact in

Papers in

    • Natural Language Processing Techniques 23
    • Topic Modeling 22
    • Semantic Web and Ontologies 7
    • Speech and dialogue systems 5
    • Advanced Text Analysis Techniques 2
    • Text Readability and Simplification 2
    • Machine Learning in Materials Science 2

Tim O’Gorman

25 papers receiving 325 citations

Peers

Tim O’Gorman
Comparison fields: 5 of 34
  • Artificial Intelligence 338
  • Management Science and Operations Research 26
  • Computer Vision and Pattern Recognition 39
  • General Social Sciences 5
  • Information Systems 24
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Citations per year

Countries citing papers authored by Tim O’Gorman

Since Specialization
Citations

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

Fields of papers citing papers by Tim O’Gorman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tim O’Gorman. 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 Tim O’Gorman. The network helps show where Tim O’Gorman may publish in the future.

Co-authors

The 25 scholars most cited alongside Tim O’Gorman, 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 Tim O’Gorman Line = papers co-authored together Tim O’Gorman 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 201678
2 201945
3 202034
4
AMR Beyond the Sentence: the Multi-sentence AMR corpus
201822
5 201421
6 202120
7 201618
8
NAACL HLT 2015, The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, USA, May 31 - June 5, 2015
201518
9 202015
10 201613
11 201612
12 20219
13 20228
14 20178
15 20187
16
The New Propbank: Aligning Propbank with AMR through POS Unification
20186
17 20136
18 20225
19 20214
20 20152

About Tim O’Gorman

Tim O’Gorman is a scholar working on Artificial Intelligence, Materials Chemistry, Molecular Biology, Information Systems and Anthropology, having authored 27 papers that have together received 357 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (23 papers), Topic Modeling (22 papers), Semantic Web and Ontologies (7 papers), Speech and dialogue systems (5 papers), Advanced Text Analysis Techniques (2 papers), Text Readability and Simplification (2 papers), Machine Learning in Materials Science (2 papers) and Language and cultural evolution (1 paper). The work is most often cited by research in Artificial Intelligence (338 citations), Management Science and Operations Research (26 citations), Computer Vision and Pattern Recognition (39 citations), General Social Sciences (5 citations) and Information Systems (24 citations). Tim O’Gorman has collaborated with scholars based in United States, Norway and Czechia. Frequent co-authors include Martha Palmer, Nathan Schneider, Nianwen Xue, Stephan Oepen, Jan Hajič, Daniel Hershcovich, Omri Abend, Jeffrey Flanigan, Kevin Knight and Kira Griffitt. Their work appears in journals such as Language Resources and Evaluation, International Journal of Speech Technology, KI - Künstliche Intelligenz, International Conference on Computational Linguistics and Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

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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