2020 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to natural language processing and computational linguistics, and development of widely used techniques in text summarization, question answering, and education.
2018 - Fellow of the American Association for the Advancement of Science (AAAS)
2015 - ACM Fellow For contributions to natural language processing and computational linguistics
2008 - ACM Distinguished Member
Dragomir R. Radev mainly investigates Artificial intelligence, Information retrieval, Automatic summarization, Natural language processing and Multi-document summarization. Dragomir R. Radev studies Artificial intelligence, namely Cosine similarity. His Information retrieval research is multidisciplinary, relying on both Cohesion, Computational linguistics, Set and Citation.
The concepts of his Automatic summarization study are interwoven with issues in Sentence, Prestige, World Wide Web and Cluster analysis. The Natural language processing study combines topics in areas such as Graph and Taxonomy. His Multi-document summarization study incorporates themes from Paraphrase, Identity and Identification.
Dragomir R. Radev spends much of his time researching Artificial intelligence, Natural language processing, Information retrieval, Automatic summarization and Question answering. Dragomir R. Radev focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Graph and, in certain cases, Centrality. His study looks at the relationship between Natural language processing and fields such as Domain, as well as how they intersect with chemical problems.
Within one scientific family, Dragomir R. Radev focuses on topics pertaining to Citation under Information retrieval, and may sometimes address concerns connected to Field. His studies in Automatic summarization integrate themes in fields like Salient, Lexical similarity and World Wide Web. His work deals with themes such as Document retrieval and Search engine, which intersect with Question answering.
His primary areas of investigation include Artificial intelligence, Natural language processing, Information retrieval, Domain and SQL. His work on Artificial intelligence is being expanded to include thematically relevant topics such as Machine learning. His Natural language processing research includes elements of Dependency, Context, Word and Deep learning.
His study in the field of Information extraction is also linked to topics like Key. His research integrates issues of Natural language, Task and Test set in his study of SQL. His primary area of study in Automatic summarization is in the field of Multi-document summarization.
His scientific interests lie mostly in Artificial intelligence, Natural language processing, Information retrieval, SQL and Automatic summarization. His research related to Sentence, Coreference and Artificial neural network might be considered part of Artificial intelligence. The Natural language processing study combines topics in areas such as Context, Antecedent, Resolution and Cluster analysis.
His Information retrieval research includes elements of Computational linguistics and Benchmark. His research in SQL intersects with topics in Domain, Machine learning, Test set and Natural language. His research on Automatic summarization focuses in particular on Multi-document summarization.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
LexRank: graph-based lexical centrality as salience in text summarization
Günes Erkan;Dragomir R. Radev.
Journal of Artificial Intelligence Research (2004)
Centroid-based summarization of multiple documents
Dragomir R. Radev;Hongyan Jing;Małgorzata Styś;Daniel Tam.
Information Processing and Management (2004)
TimeML: Robust Specification of Event and Temporal Expressions in Text
James Pustejovsky;José M. Castaño;Robert Ingria;Roser Saurí.
New Directions in Question Answering (2003)
Rumor has it: Identifying Misinformation in Microblogs
Vahed Qazvinian;Emily Rosengren;Dragomir R. Radev;Qiaozhu Mei.
empirical methods in natural language processing (2011)
How to Analyze Political Attention with Minimal Assumptions and Costs
Kevin M. Quinn;Burt L. Monroe;Michael Colaresi;Michael H. Crespin.
American Journal of Political Science (2010)
Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies
Dragomir R. Radev;Hongyan Jing;Malgorzata Budzikowska.
north american chapter of the association for computational linguistics (2000)
Generating natural language summaries from multiple on-line sources
Dragomir R. Radev;Kathleen R. McKeown.
natural language generation (1998)
Introduction to the special issue on summarization
Dragomir R. Radev;Eduard Hovy;Kathleen McKeown.
Computational Linguistics (2002)
Generating summaries of multiple news articles
Kathleen McKeown;Dragomir R. Radev.
international acm sigir conference on research and development in information retrieval (1995)
System, method and program product for interactive natural dialog
Joyce Yue Chai;Sunil Subramanyam Govindappa;Nandakishore Kambhatla;Tetsunosuke Fujisaki.
Profile was last updated on December 6th, 2021.
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