2019 - ACM Fellow For contributions to natural language processing, including coreference resolution, information and opinion extraction
Claire Cardie mainly investigates Artificial intelligence, Natural language processing, Task, Machine learning and Information retrieval. Her work focuses on many connections between Artificial intelligence and other disciplines, such as Context, that overlap with her field of interest in Inference. Her biological study spans a wide range of topics, including Scheme, Agreement and Coreference.
Her research investigates the connection with Coreference and areas like Determiner phrase which intersect with concerns in Cluster analysis. As part of the same scientific family, Claire Cardie usually focuses on Task, concentrating on Identification and intersecting with Conditional random field and Information extraction. Her studies in Information retrieval integrate themes in fields like Vision statement and Aggregate.
Claire Cardie spends much of her time researching Artificial intelligence, Natural language processing, Task, Machine learning and Information retrieval. Artificial intelligence connects with themes related to Context in her study. Her Noun phrase, Sentence, Parsing and Information extraction study in the realm of Natural language processing interacts with subjects such as Structure.
Her work deals with themes such as Domain, Identification, Similarity, Relation and Event, which intersect with Task. Claire Cardie combines subjects such as Inference and Data mining with her study of Machine learning. Her research integrates issues of Annotation and Opinion analysis in her study of Information retrieval.
Her main research concerns Artificial intelligence, Natural language processing, Task, Context and Argument. Claire Cardie specializes in Artificial intelligence, namely Transfer of learning. Her Natural language processing study incorporates themes from Set, Leverage, Comprehension and Coreference.
Her Task research is multidisciplinary, relying on both Language model, Question answering and Domain. Her research investigates the connection between Context and topics such as Task that intersect with problems in Image and Image generation. Her Argument research includes themes of Persuasion, Politics, Public debate, Argumentative and Set.
Her primary scientific interests are in Artificial intelligence, Natural language processing, Task, Information retrieval and Focus. Her research on Artificial intelligence frequently links to adjacent areas such as Machine learning. Her research in Machine learning intersects with topics in Generalization, Inference and Natural language.
Her work carried out in the field of Natural language processing brings together such families of science as Paragraph, Leverage, Artificial neural network, Set and Similarity. In her research on the topic of Task, Language model, Comprehension, Reading, Code and Domain knowledge is strongly related with Domain. Her study in the field of Question answering also crosses realms of Subject areas.
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.
Constrained K-means Clustering with Background Knowledge
Kiri Wagstaff;Claire Cardie;Seth Rogers;Stefan Schrödl.
international conference on machine learning (2001)
Annotating Expressions of Opinions and Emotions in Language
Janyce Wiebe;Theresa Wilson;Claire Cardie.
language resources and evaluation (2005)
Improving Machine Learning Approaches to Coreference Resolution
Vincent Ng;Claire Cardie.
meeting of the association for computational linguistics (2002)
Clustering with Instance-Level Constraints
Kiri Wagstaff;Claire Cardie.
national conference on artificial intelligence (2000)
OpinionFinder: A System for Subjectivity Analysis
Theresa Wilson;Paul Hoffmann;Swapna Somasundaran;Jason Kessler.
empirical methods in natural language processing (2005)
Finding Deceptive Opinion Spam by Any Stretch of the Imagination
Myle Ott;Yejin Choi;Claire Cardie;Jeffrey T. Hancock.
meeting of the association for computational linguistics (2011)
Using decision trees to improve case-based learning
Claire Cardie.
international conference on machine learning (1993)
Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns
Yejin Choi;Claire Cardie;Ellen Riloff;Siddharth Patwardhan.
empirical methods in natural language processing (2005)
Empirical Methods in Information Extraction
Claire Cardie.
Ai Magazine (1997)
Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis
Yejin Choi;Claire Cardie.
empirical methods in natural language processing (2008)
Profile was last updated on December 6th, 2021.
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