D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 51 Citations 11,550 136 World Ranking 2761 National Ranking 1462

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Programming language
  • Machine learning

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.

His most cited work include:

  • System for generation of user profiles for a system for customized electronic identification of desirable objects (2433 citations)
  • Secure data interchange (1802 citations)
  • System for the automatic determination of customized prices and promotions (953 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (59.50%)
  • Natural language processing (43.50%)
  • Parsing (18.50%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (59.50%)
  • Natural language processing (43.50%)
  • Language model (7.50%)

In recent papers he was focusing on the following fields of study:

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.

Between 2017 and 2021, his most popular works were:

  • UniMorph 2.0: Universal Morphology (51 citations)
  • Are All Languages Equally Hard to Language-Model? (51 citations)
  • Specializing Word Embeddings (for Parsing) by Information Bottleneck (26 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Programming language
  • Machine learning

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.

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.

Best Publications

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.
(1995)

2703 Citations

Secure data interchange

Frederick S. M. Herz;Walter Paul Labys;David C. Parkes;Sampath Kannan.
(2000)

2375 Citations

System for the automatic determination of customized prices and promotions

Frederick Herz;Jason Eisner;Lyle Unger;Walter Paul Labys.
(1998)

1293 Citations

Three new probabilistic models for dependency parsing: an exploration

Jason M. Eisner.
international conference on computational linguistics (1996)

919 Citations

Contrastive Estimation: Training Log-Linear Models on Unlabeled Data

Noah A. Smith;Jason Eisner.
meeting of the association for computational linguistics (2005)

378 Citations

Learning Non-Isomorphic Tree Mappings for Machine Translation

Jason Eisner.
meeting of the association for computational linguistics (2003)

310 Citations

The neural hawkes process: a neurally self-modulating multivariate point process

Hongyuan Mei;Jason Eisner.
neural information processing systems (2017)

235 Citations

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)

215 Citations

Parameter Estimation for Probabilistic Finite-State Transducers

Jason Eisner.
meeting of the association for computational linguistics (2002)

204 Citations

Efficient Parsing for Bilexical Context-Free Grammars and Head Automaton Grammars

Jason Eisner;Giorgio Satta.
meeting of the association for computational linguistics (1999)

184 Citations

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