What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Natural language processing
- Machine learning
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 most cited work include:
- Evaluating Content Selection in Summarization: The Pyramid Method (462 citations)
- Automatic Summarization (357 citations)
- A SURVEY OF TEXT SUMMARIZATION TECHNIQUES (323 citations)
What are the main themes of her work throughout her whole career to date?
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.
She most often published in these fields:
- Artificial intelligence (54.97%)
- Natural language processing (47.02%)
- Automatic summarization (35.76%)
What were the highlights of her more recent work (between 2016-2021)?
- Artificial intelligence (54.97%)
- Natural language processing (47.02%)
- Randomized controlled trial (5.96%)
In recent papers she was focusing on the following fields of study:
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.
Between 2016 and 2021, her most popular works were:
- A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature. (68 citations)
- Aggregating and Predicting Sequence Labels from Crowd Annotations. (37 citations)
- How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature (19 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Natural language processing
- Machine learning
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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