2009 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to many aspects of the theory and practice of machine learning.
Artificial intelligence, Machine learning, Information retrieval, Natural language processing and World Wide Web are his primary areas of study. His research integrates issues of Named-entity recognition and Graph in his study of Artificial intelligence. His Machine learning research is multidisciplinary, incorporating elements of Classifier, Graph, Principle of maximum entropy and Multi-task learning.
His study on Recommender system is often connected to Heuristics as part of broader study in Information retrieval. William W. Cohen has researched Natural language processing in several fields, including Feature engineering, Recurrent neural network and Categorization. His studies in World Wide Web integrate themes in fields like Active learning, Instance-based learning, Set and Competence.
His scientific interests lie mostly in Artificial intelligence, Natural language processing, Machine learning, Information retrieval and Theoretical computer science. His study connects Graph and Artificial intelligence. His Natural language processing research integrates issues from Context and Word.
The study incorporates disciplines such as Multi-task learning and Classifier in addition to Machine learning. William W. Cohen combines subjects such as World Wide Web and Data mining with his study of Information retrieval. His study in Theoretical computer science is interdisciplinary in nature, drawing from both Simple, Scalability and Learnability.
William W. Cohen mainly focuses on Artificial intelligence, Natural language processing, Knowledge base, Question answering and Machine learning. Language model, Word, Deep learning, Benchmark and Probabilistic logic are among the areas of Artificial intelligence where William W. Cohen concentrates his study. The concepts of his Natural language processing study are interwoven with issues in Recurrent neural network, Graph, Reading comprehension, Task and Transfer of learning.
His biological study spans a wide range of topics, including Scalability, Theoretical computer science and Inference. Question answering is a subfield of Information retrieval that William W. Cohen studies. His work on Semi-supervised learning as part of general Machine learning research is frequently linked to Heuristics, thereby connecting diverse disciplines of science.
His primary areas of investigation include Artificial intelligence, Natural language processing, Machine learning, Question answering and Benchmark. His study in Artificial intelligence concentrates on Embedding, Inference, Training set, Probabilistic logic and Treebank. His research in Natural language processing intersects with topics in Recurrent neural network, Baseline, Character, Reading comprehension and Spelling.
His Machine learning research incorporates themes from Language model and Generative grammar. His Question answering study is concerned with the larger field of Information retrieval. In his work, Data mining is strongly intertwined with Generator, which is a subfield of Semi-supervised learning.
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Fast effective rule induction
William W. Cohen.
international conference on machine learning (1995)
A comparison of string distance metrics for name-matching tasks
William W. Cohen;Pradeep Ravikumar;Stephen E. Fienberg.
IIWEB'03 Proceedings of the 2003 International Conference on Information Integration on the Web (2003)
Recommendation as classification: using social and content-based information in recommendation
Chumki Basu;Haym Hirsh;William Cohen.
national conference on artificial intelligence (1998)
Learning to order things
William W. Cohen;Robert E. Schapire;Yoram Singer.
Journal of Artificial Intelligence Research (1999)
Context-sensitive learning methods for text categorization
William W. Cohen;Yoram Singer.
ACM Transactions on Information Systems (1999)
Semi-Markov Conditional Random Fields for Information Extraction
Sunita Sarawagi;William W. Cohen.
neural information processing systems (2004)
Learning Rules that Classify E-Mail
William W. Cohen.
(1996)
Adaptive name matching in information integration
M. Bilenko;R. Mooney;W. Cohen;P. Ravikumar.
IEEE Intelligent Systems (2003)
Never-ending learning
T. Mitchell;W. Cohen;E. Hruschka;P. Talukdar.
Communications of The ACM (2018)
Never-ending learning
T. Mitchell;W. Cohen;E. Hruschka;P. Talukdar.
national conference on artificial intelligence (2015)
Carnegie Mellon University
Carnegie Mellon University
Cornell University
University of California, Santa Barbara
Carnegie Mellon University
Princeton University
Carnegie Mellon University
Carnegie Mellon University
Indian Institute of Science Bangalore
University of Nottingham Malaysia Campus
Profile was last updated on December 6th, 2021.
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