Beth Sundheim

32 papers receiving 1.6k citations

Hit Papers

Message Understanding Conference-619962026200620161996250500750

Peers

Beth Sundheim
Comparison fields: 5 of 110
  • Artificial Intelligence 1.6k
  • Information Systems 367
  • Molecular Biology 209
  • Management Science and Operations Research 132
  • Signal Processing 95
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Nancy Chinchor United States
Janez Brank Slovenia
Stephen H. Bach United States
Enrique Amigó Spain
Aliaksei Severyn Italy
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William Webber Australia
Josiane Mothe France
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Countries citing papers authored by Beth Sundheim

Since Specialization
Citations

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

Fields of papers citing papers by Beth Sundheim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Beth Sundheim

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1
Gazetteer Linkage to WordNet
5
2
Resources for Place Name Analysis
5
3 26
4
2003 Standard for the Annotation of Temporal Expressions
95
5
Annotating temporal information: from theory to practice
8
6 1
7 149
8
Event99: A proposed event indexing task for broadcast news
6
9
Task-oriented text analysis evaluation
1
10 8
11 23
12
Message Understanding Conference (MUC) tests of discourse processing
12
13
Named entity task definition, version 2.1
25
14 57
15 2
16 16
17 8
18 17
19 41
20 13

About Beth Sundheim

Beth Sundheim is a scholar working on Artificial Intelligence, Geography, Planning and Development and Language and Linguistics, having authored 33 papers that have together received 1.8k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (21 papers), Semantic Web and Ontologies (10 papers) and Speech and dialogue systems (6 papers). The work is most often cited by research in Artificial Intelligence (1.6k citations), Information Systems (367 citations) and Management Science and Operations Research (132 citations). Beth Sundheim has collaborated with scholars based in United States. Frequent co-authors include Ralph Grishman, Nancy Chinchor, Inderjeet Mani, David House, Gary Klein, Lynette Hirschman, Lisa Ferro, George D. Wilson, Laurie Gerber and James Pustejovsky. Their work appears in journals such as Language Resources and Evaluation, Natural Language Engineering and ACM Transactions on Asian Language Information Processing.

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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