2014 - Fellow of the American Association for the Advancement of Science (AAAS)
2011 - ACM Fellow For contributions to machine learning and natural language processing.
Artificial intelligence, Machine learning, Natural language processing, Inference and Context are his primary areas of study. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Set and Pattern recognition. Dan Roth works mostly in the field of Machine learning, limiting it down to concerns involving Training set and, occasionally, Grammar, Classifier, Error detection and correction and Range.
His studies deal with areas such as Textual entailment, Entity linking, Information retrieval and Hierarchical classifier as well as Natural language processing. His Inference study combines topics from a wide range of disciplines, such as F1 score, Relation, Linear programming, Semantic role labeling and Integer programming. His Context research is multidisciplinary, relying on both Annotation, Focus and Identification.
Dan Roth mainly focuses on Artificial intelligence, Natural language processing, Machine learning, Inference and Natural language. His study brings together the fields of Context and Artificial intelligence. His Natural language processing research incorporates elements of Named-entity recognition, Textual entailment, Word and Information retrieval.
His Machine learning study frequently involves adjacent topics like Training set. Much of his study explores Inference relationship to Theoretical computer science. His Semi-supervised learning study combines topics in areas such as Active learning and Algorithmic learning theory.
His primary areas of study are Artificial intelligence, Natural language processing, Natural language, Context and Named-entity recognition. He has included themes like Event and Machine learning in his Artificial intelligence study. His work in Machine learning addresses issues such as Inference, which are connected to fields such as Supervised learning.
The concepts of his Natural language processing study are interwoven with issues in Object, Scheme, Set and Coreference. His work on Natural language understanding as part of general Natural language study is frequently connected to Duration, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His study explores the link between Context and topics such as Structure that cross with problems in Structured prediction.
Dan Roth mostly deals with Artificial intelligence, Natural language processing, Natural language, Named-entity recognition and Context. His Artificial intelligence study incorporates themes from Event and Machine learning. His studies in Machine learning integrate themes in fields like Relationship extraction and Word.
His Natural language processing study integrates concerns from other disciplines, such as Probabilistic logic, Set and Coreference. The study incorporates disciplines such as Window, World Wide Web and Supervised learning in addition to Natural language. The various areas that he examines in his Context study include Textual entailment, Hindi, Learning models, Empirical research and Conceptualization.
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Design Challenges and Misconceptions in Named Entity Recognition
Lev Ratinov;Dan Roth.
conference on computational natural language learning (2009)
Learning question classifiers
Xin Li;Dan Roth.
international conference on computational linguistics (2002)
Learning to detect objects in images via a sparse, part-based representation
S. Agarwal;A. Awan;D. Roth.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)
Emotions from Text: Machine Learning for Text-based Emotion Prediction
Cecilia Ovesdotter Alm;Dan Roth;Richard Sproat.
empirical methods in natural language processing (2005)
Local and Global Algorithms for Disambiguation to Wikipedia
Lev Ratinov;Dan Roth;Doug Downey;Mike Anderson.
meeting of the association for computational linguistics (2011)
Learning a Sparse Representation for Object Detection
Shivani Agarwal;Dan Roth.
european conference on computer vision (2002)
On the hardness of approximate reasoning
Artificial Intelligence (1996)
A SNoW-Based Face Detector
Ming-Hsuan Yang;Dan Roth;Narendra Ahuja.
neural information processing systems (1999)
The importance of syntactic parsing and inference in semantic role labeling
Vasin Punyakanok;Vasin Punyakanok;Vasin Punyakanok;Dan Roth;Dan Roth;Dan Roth;Wen-tau Yih;Wen-tau Yih;Wen-tau Yih.
Computational Linguistics (2008)
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Andrew R. Golding;Dan Roth.
Machine Learning (1999)
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