What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Natural language processing
- Programming language
Her scientific interests lie mostly in Artificial intelligence, Natural language processing, Automatic summarization, Information retrieval and Natural language.
Her Artificial intelligence study integrates concerns from other disciplines, such as Conjunction and Set.
Her biological study spans a wide range of topics, including Speech recognition, Conversation and Identification.
Her Automatic summarization study combines topics in areas such as Event, Data mining, Cluster analysis and Reading.
Her Information retrieval research integrates issues from Natural language generation and Selection.
Kathleen R. McKeown combines subjects such as Database structure, Paragraph, Rhetorical question and Text linguistics with her study of Natural language.
Her most cited work include:
- Predicting the Semantic Orientation of Adjectives (1075 citations)
- Text generation: using discourse strategies and focus constraints to generate natural language text (542 citations)
- Predicting the semantic orientation of adjectives (511 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Artificial intelligence, Natural language processing, Automatic summarization, Information retrieval and Natural language.
Her study focuses on the intersection of Artificial intelligence and fields such as Speech recognition with connections in the field of Segmentation.
The concepts of her Natural language processing study are interwoven with issues in Context and Focus.
Her work in the fields of Sentence extraction overlaps with other areas such as Digital library.
Her work on Multi-document summarization, Information extraction and Question answering as part of her general Information retrieval study is frequently connected to Information technology, thereby bridging the divide between different branches of science.
Her Natural language research includes themes of Expert system and Human–computer interaction.
She most often published in these fields:
- Artificial intelligence (60.12%)
- Natural language processing (54.84%)
- Automatic summarization (24.63%)
What were the highlights of her more recent work (between 2016-2021)?
- Artificial intelligence (60.12%)
- Natural language processing (54.84%)
- Social media (5.87%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Artificial intelligence, Natural language processing, Social media, Automatic summarization and Speech recognition.
Her Artificial intelligence research includes elements of Machine learning and Set.
Kathleen R. McKeown is interested in Machine translation, which is a field of Natural language processing.
The various areas that Kathleen R. McKeown examines in her Social media study include Context, Argumentation theory, Expression, Aggression and Data science.
Her Automatic summarization research focuses on Consistency and how it relates to Question answering and Information retrieval.
The study incorporates disciplines such as Natural language generation, Segmentation, Noise and Sample in addition to Speech recognition.
Between 2016 and 2021, her most popular works were:
- Content Selection in Deep Learning Models of Summarization (86 citations)
- Human-centric justification of machine learning predictions (30 citations)
- Predictive Embeddings for Hate Speech Detection on Twitter. (29 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Natural language processing
- Programming language
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Social media, Word and Speech recognition.
Her study in the field of Domain knowledge also crosses realms of Human centric.
Her work deals with themes such as Deep learning and Selection, which intersect with Natural language processing.
Her Social media study also includes
- Expression that intertwine with fields like Measure, Urban green space, Context and Cognitive psychology,
- Aggression which connect with Subject-matter expert and Modality,
- Data science that intertwine with fields like Supervised learning, Stress and Identification,
- Computational sociology, Dialog box, World Wide Web and Conversation most often made with reference to Argumentation theory.
Her Word research incorporates themes from Computational linguistics, Artificial neural network, Machine translation and Set.
Her studies in Speech recognition integrate themes in fields like Feature, Decoding methods and Pooling.
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