Jason Eisner focuses on Artificial intelligence, Natural language processing, Algorithm, Parsing and Machine learning. The study of Artificial intelligence is intertwined with the study of Pattern recognition in a number of ways. His Natural language processing research is multidisciplinary, incorporating perspectives in Generative grammar and Training set.
The study incorporates disciplines such as Theoretical computer science, Machine translation, Solver and Dependency grammar in addition to Algorithm. His Parsing research includes elements of Sentence and Rule-based machine translation. His research investigates the link between Sentence and topics such as Translation that cross with problems in Word.
Jason Eisner mainly focuses on Artificial intelligence, Natural language processing, Parsing, Algorithm and Machine learning. His research is interdisciplinary, bridging the disciplines of Pattern recognition and Artificial intelligence. Jason Eisner combines subjects such as Grammar, Word and Generative grammar with his study of Natural language processing.
His work in Parsing is not limited to one particular discipline; it also encompasses Syntax. His Algorithm study combines topics from a wide range of disciplines, such as Automaton, Theoretical computer science and String. Jason Eisner studied Machine learning and Inference that intersect with Graphical model and Belief propagation.
His primary areas of investigation include Artificial intelligence, Natural language processing, Language model, Generative model and Word. His research integrates issues of Surface, Set and Scripting language in his study of Artificial intelligence. His Natural language processing research is multidisciplinary, incorporating elements of Context, Supervised learning and Morphology.
His Language model study combines topics in areas such as Initialization, Vocabulary and Expression. Jason Eisner has researched Generative model in several fields, including Consistency, Estimation theory and Punctuation. In his study, Word embedding, Dimensionality reduction, Discriminative model and Syntax is inextricably linked to Information bottleneck method, which falls within the broad field of Parsing.
Jason Eisner spends much of his time researching Natural language processing, Artificial intelligence, Parsing, Morphology and Language model. His Natural language processing research incorporates elements of Supervised learning, Word, Context and Linguistic sequence complexity. His Word research integrates issues from Information bottleneck method, Syntax, Set and Dimensionality reduction.
His research on Artificial intelligence frequently links to adjacent areas such as Schema. The study incorporates disciplines such as Constructed language and Discriminative model in addition to Parsing. His Language model study incorporates themes from Sentence, Vocabulary, Generative grammar and Written language.
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System and method for scheduling broadcast of and access to video programs and other data using customer profiles
Frederick Herz;Lyle Ungar;Jian Zhang;David Wachob.
Secure data interchange
Frederick S. M. Herz;Walter Paul Labys;David C. Parkes;Sampath Kannan.
System for the automatic determination of customized prices and promotions
Frederick Herz;Jason Eisner;Lyle Unger;Walter Paul Labys.
Three new probabilistic models for dependency parsing: an exploration
Jason M. Eisner.
international conference on computational linguistics (1996)
Contrastive Estimation: Training Log-Linear Models on Unlabeled Data
Noah A. Smith;Jason Eisner.
meeting of the association for computational linguistics (2005)
Learning Non-Isomorphic Tree Mappings for Machine Translation
meeting of the association for computational linguistics (2003)
The neural hawkes process: a neurally self-modulating multivariate point process
Hongyuan Mei;Jason Eisner.
neural information processing systems (2017)
Using ``Annotator Rationales'' to Improve Machine Learning for Text Categorization
Omar Zaidan;Jason Eisner;Christine Piatko.
north american chapter of the association for computational linguistics (2007)
Parameter Estimation for Probabilistic Finite-State Transducers
meeting of the association for computational linguistics (2002)
Efficient Parsing for Bilexical Context-Free Grammars and Head Automaton Grammars
Jason Eisner;Giorgio Satta.
meeting of the association for computational linguistics (1999)
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