Anni R. Coden

1.1k citations
29 papers · 838 · h-index 13

Impact in

Papers in

    • Natural Language Processing Techniques 18
    • Topic Modeling 16
    • Semantic Web and Ontologies 5
    • Advanced Text Analysis Techniques 4
    • Speech and dialogue systems 4
    • Speech Recognition and Synthesis 3

Anni R. Coden

29 papers receiving 754 citations

Peers

Anni R. Coden
Comparison fields: 5 of 70
  • Artificial Intelligence 610
  • Health Information Management 34
  • Information Systems 160
  • Signal Processing 49
  • Molecular Biology 281
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Citations per field
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Citations per year

Countries citing papers authored by Anni R. Coden

Since Specialization
Citations

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

Fields of papers citing papers by Anni R. Coden

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anni R. Coden. 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 Anni R. Coden. The network helps show where Anni R. Coden may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2000174
2 2008153
3 201068
4
The Use of Predictive Annotation for Question Answering in TREC8.
199967
5 200856
6 200445
7 200543
8 201234
9 200226
10 200123
11 200621
12 201716
13 200215
14 201712
15 200112
16 200211
17 20149
18 20038
19 20047
20 20056

About Anni R. Coden

Anni R. Coden is a scholar working on Artificial Intelligence, Signal Processing, Language and Linguistics, Molecular Biology and Health Information Management, having authored 29 papers that have together received 838 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (18 papers), Topic Modeling (16 papers), Biomedical Text Mining and Ontologies (6 papers), Semantic Web and Ontologies (5 papers), Advanced Text Analysis Techniques (4 papers), Speech and dialogue systems (4 papers), Speech Recognition and Synthesis (3 papers) and Lexicography and Language Studies (2 papers). The work is most often cited by research in Artificial Intelligence (610 citations), Health Information Management (34 citations), Information Systems (160 citations), Signal Processing (49 citations) and Molecular Biology (281 citations). Anni R. Coden has collaborated with scholars based in United States, Israel and India. Frequent co-authors include Eric W. Brown, Dragomir Radev, John M. Prager, Michael Tanenblatt, Guergana Savova, Piet C. de Groen, Christopher G. Chute, Wei Guan, James J Masanz and Daniel Gruhl. Their work appears in journals such as Journal of Biomedical Informatics, IBM Systems Journal, ACM SIGIR Forum, Information Retrieval and IBM Journal of Research and Development.

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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