Gina‐Anne Levow

70 papers receiving 968 citations

Peers

Gina‐Anne Levow
Comparison fields: 5 of 72
  • Artificial Intelligence 975
  • Experimental and Cognitive Psychology 223
  • Signal Processing 132
  • Computer Vision and Pattern Recognition 107
  • Human-Computer Interaction 104
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Mariët Theune Netherlands
E. Shriberg United States
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Gina‐Anne Levow relative to Mariët Theune Netherlands Mariët Theune's profile →
Citations per field
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Citations per year

Countries citing papers authored by Gina‐Anne Levow

Since Specialization
Citations

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

Fields of papers citing papers by Gina‐Anne Levow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gina‐Anne Levow. 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 Gina‐Anne Levow. The network helps show where Gina‐Anne Levow may publish in the future.

Co-authorship network of co-authors of Gina‐Anne Levow

This figure shows the co-authorship network connecting the top 25 collaborators of Gina‐Anne Levow. A scholar is included among the top collaborators of Gina‐Anne Levow 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 Gina‐Anne Levow. Gina‐Anne Levow 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
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5
UWCL at MediaEval 2013: Similar Segments in Social Speech Task
2
6
Bridging Gaps for Spoken Dialog System Frameworks in Instructional Settings
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7
Contrasting Multi-Lingual Prosodic Cues to Predict Verbal Feedback for Rapport
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8
Automatic Prosodic Labeling with Conditional Random Fields and Rich Acoustic Features
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9
Topic Segmentation with Hybrid Document Indexing
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10 1
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The Third International Chinese Language Processing Bakeoff: Word Segmentation and Named Entity Recognition
196
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University of Chicago at TREC 2004: HARD Track.
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14
University of Chicago at NTCIR4 CLIR: Multi-Scale Query Expansion
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Combining Prosodic and Text Features for Segmentation of Mandarin Broadcast News
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Prosodic Cues to Discourse Segment Boundaries in Human-Computer Dialogue
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Learning to Speak to a Spoken Language System: Vocabulary Convergence in Novice Users
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CLEF Experiments at the University of Maryland: Statistical Stemming and Back-off Translation Strategies.
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About Gina‐Anne Levow

Gina‐Anne Levow is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology and Signal Processing, having authored 78 papers that have together received 1.1k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (35 papers), Speech and dialogue systems (27 papers) and Topic Modeling (26 papers). The work is most often cited by research in Artificial Intelligence (975 citations), Human-Computer Interaction (104 citations) and Experimental and Cognitive Psychology (223 citations). Gina‐Anne Levow has collaborated with scholars based in United States, Hong Kong and Netherlands. Frequent co-authors include Sharon Oviatt, Nicole Yankelovich, Matt Marx, Dinoj Surendran, Margaret R. MacEachern, Douglas W. Oard, Philip Resnik, Helen Meng, Maxine Eskénazi and David Suendermann. Their work appears in journals such as The Journal of the Acoustical Society of America, Information Processing & Management and IEEE Journal of Selected Topics in Signal 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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