Ani Nenkova mostly deals with Automatic summarization, Artificial intelligence, Information retrieval, Natural language processing and Multi-document summarization. Her Automatic summarization study integrates concerns from other disciplines, such as Feature, Baseline, Data mining, Selection and Sentence. Her work on Document summarization as part of general Artificial intelligence research is frequently linked to Pyramid, Improved performance and Graph, bridging the gap between disciplines.
The concepts of her Information retrieval study are interwoven with issues in Pyramid and Selection. Her Natural language processing research includes themes of Discourse relation and Readability. Her Multi-document summarization research is multidisciplinary, incorporating perspectives in Information access, Text categorization, State and Cluster analysis.
Her main research concerns Artificial intelligence, Natural language processing, Automatic summarization, Information retrieval and Multi-document summarization. Her Artificial intelligence research incorporates themes from Machine learning, Context, Speech recognition and Discourse relation. Her study in the field of Sentence, Information extraction and Syntax is also linked to topics like Sequence.
Her research investigates the connection between Sentence and topics such as Machine translation that intersect with issues in Fluency. Her Automatic summarization research integrates issues from Feature and Data mining. The study incorporates disciplines such as Pyramid, Cluster analysis and Selection in addition to Information retrieval.
Ani Nenkova mainly focuses on Artificial intelligence, Natural language processing, Randomized controlled trial, Information extraction and Clinical trial. Her research integrates issues of Domain and Verbosity in her study of Artificial intelligence. Her work on Automatic summarization and Relationship extraction as part of general Natural language processing research is often related to Set, thus linking different fields of science.
Her work in Automatic summarization covers topics such as Baseline which are related to areas like Multi-document summarization and Heuristic. Her Information extraction research incorporates elements of Question answering, Annotation and Classifier. Her research in Information retrieval intersects with topics in Robustness and Vietnamese.
Her primary scientific interests are in Artificial intelligence, Natural language processing, Automatic summarization, Information retrieval and Named-entity recognition. Her Artificial intelligence study incorporates themes from Big Five personality traits and Debiasing. In the field of Natural language processing, her study on Information extraction overlaps with subjects such as Sequence and Set.
Her Automatic summarization study combines topics in areas such as Normalization and Verbosity. Her work carried out in the field of Information retrieval brings together such families of science as Robustness and Vietnamese. Ani Nenkova combines subjects such as Context, Linguistic context and Interpretability with her study of Named-entity recognition.
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Ani Nenkova;Sameer Maskey;Yang Liu.
Evaluating Content Selection in Summarization: The Pyramid Method
Ani Nenkova;Rebecca J. Passonneau.
north american chapter of the association for computational linguistics (2004)
A SURVEY OF TEXT SUMMARIZATION TECHNIQUES
Ani Nenkova;Kathleen R. McKeown.
Mining Text Data (2012)
Revisiting Readability: A Unified Framework for Predicting Text Quality
Emily Pitler;Ani Nenkova.
empirical methods in natural language processing (2008)
Tracking and summarizing news on a daily basis with Columbia's Newsblaster
Kathleen R. McKeown;Regina Barzilay;David Evans;Vasileios Hatzivassiloglou.
international conference on human language technology research (2002)
The Pyramid Method: Incorporating human content selection variation in summarization evaluation
Ani Nenkova;Rebecca Passonneau;Kathleen McKeown.
ACM Transactions on Speech and Language Processing (2007)
Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion
Lucy Vanderwende;Hisami Suzuki;Chris Brockett;Ani Nenkova.
Information Processing and Management (2007)
Automatic sense prediction for implicit discourse relations in text
Emily Pitler;Annie Louis;Ani Nenkova.
international joint conference on natural language processing (2009)
The Impact of Frequency on Summarization
A. Nenkova;L. Vanderwende;Lucy Vanderwende.
Using Syntax to Disambiguate Explicit Discourse Connectives in Text
Emily Pitler;Ani Nenkova.
meeting of the association for computational linguistics (2009)
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