Raymond J. Mooney spends much of his time researching Artificial intelligence, Natural language processing, Machine learning, Natural language and Parsing. His research in Artificial intelligence intersects with topics in Multi-task learning and Pattern recognition. Raymond J. Mooney has included themes like Semantics, Inductive logic programming and Active learning in his Natural language processing study.
His Machine learning study integrates concerns from other disciplines, such as Active learning and Training set. His Natural language research incorporates themes from Annotation, Picture language, Object, Text mining and Kernel method. His Parsing study combines topics in areas such as Executable, Grammar, Rule-based machine translation and Machine translation.
Raymond J. Mooney focuses on Artificial intelligence, Natural language processing, Machine learning, Natural language and Parsing. His Artificial intelligence research includes elements of Statistical relational learning and Pattern recognition. His work focuses on many connections between Natural language processing and other disciplines, such as Context, that overlap with his field of interest in Perception.
His research investigates the connection between Machine learning and topics such as Data mining that intersect with problems in Correlation clustering, Fuzzy clustering and Cluster analysis. His study explores the link between Natural language and topics such as Human–computer interaction that cross with problems in Robot, Human–robot interaction and Dialog box. His Parsing research is multidisciplinary, relying on both Syntax and Formal grammar, Grammar.
Raymond J. Mooney mostly deals with Artificial intelligence, Natural language processing, Natural language, Human–computer interaction and Machine learning. His work in Probabilistic logic, Inference, Parsing, Robot and Training set are all subfields of Artificial intelligence research. His Natural language processing research is multidisciplinary, incorporating perspectives in Rule of inference, Distributional semantics and Scripting language.
The study incorporates disciplines such as Executable, Code, Object, Source code and Reinforcement learning in addition to Natural language. His Human–computer interaction research is multidisciplinary, incorporating elements of Active learning, Dialog box and Human–robot interaction. His work carried out in the field of Machine learning brings together such families of science as Multi-task learning and Abductive reasoning.
His primary areas of investigation include Artificial intelligence, Natural language processing, Natural language, Text corpus and Human–computer interaction. Recurrent neural network, Closed captioning, Cognitive neuroscience of visual object recognition, Rule of inference and Robot are subfields of Artificial intelligence in which his conducts study. The various areas that Raymond J. Mooney examines in his Natural language processing study include Probabilistic logic, Training set and Scripting language.
His study on Natural language also encompasses disciplines like
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Content-based book recommending using learning for text categorization
Raymond J. Mooney;Loriene Roy.
acm international conference on digital libraries (2000)
Explanation-Based Learning: An Alternative View
Gerald Dejong;Raymond Mooney.
Machine Learning (1986)
Adaptive duplicate detection using learnable string similarity measures
Mikhail Bilenko;Raymond J. Mooney.
knowledge discovery and data mining (2003)
Semi-supervised Clustering by Seeding
Sugato Basu;Arindam Banerjee;Raymond J. Mooney.
international conference on machine learning (2002)
Integrating constraints and metric learning in semi-supervised clustering
Mikhail Bilenko;Sugato Basu;Raymond J. Mooney.
international conference on machine learning (2004)
A probabilistic framework for semi-supervised clustering
Sugato Basu;Mikhail Bilenko;Raymond J. Mooney.
knowledge discovery and data mining (2004)
Impact of Similarity Measures on Web-page Clustering
Alexander Strehl;Joydeep Ghosh;Raymond Mooney.
A Shortest Path Dependency Kernel for Relation Extraction
Razvan Bunescu;Raymond Mooney.
empirical methods in natural language processing (2005)
Adaptive name matching in information integration
M. Bilenko;R. Mooney;W. Cohen;P. Ravikumar.
IEEE Intelligent Systems (2003)
Relational learning of pattern-match rules for information extraction
Mary Elaine Califf;Raymond J. Mooney.
national conference on artificial intelligence (1999)
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