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 58 Citations 22,185 184 World Ranking 1792 National Ranking 984

Research.com Recognitions

Awards & Achievements

2014 - Fellow of Alfred P. Sloan Foundation

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Natural language processing
  • Programming language

His primary areas of study are Natural language processing, Artificial intelligence, Machine translation, Translation and Sentence. Chris Callison-Burch works on Natural language processing which deals in particular with Paraphrase. Artificial intelligence is represented through his Semantic similarity, Speech translation, Ranking, Language model and Computer-assisted translation research.

His Speech translation research is multidisciplinary, incorporating perspectives in Pivot language, Postediting, Interactive machine translation, Hybrid machine translation and Synchronous context-free grammar. His Machine translation study integrates concerns from other disciplines, such as Ranking and Word error rate. His research in Translation intersects with topics in Direct method and Data science.

His most cited work include:

  • Moses: Open Source Toolkit for Statistical Machine Translation (4525 citations)
  • PPDB: The Paraphrase Database (565 citations)
  • Paraphrasing with Bilingual Parallel Corpora (525 citations)

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

Chris Callison-Burch focuses on Artificial intelligence, Natural language processing, Machine translation, Translation and Speech recognition. He interconnects Crowdsourcing and Machine learning in the investigation of issues within Artificial intelligence. In Natural language processing, Chris Callison-Burch works on issues like Grammar, which are connected to Synchronous context-free grammar.

His studies deal with areas such as Computational linguistics and Parsing as well as Machine translation. His studies in Sentence integrate themes in fields like Information retrieval and Fluency. His Language model research incorporates themes from Decoding methods, Discriminative model and Computer-assisted translation.

He most often published in these fields:

  • Artificial intelligence (68.78%)
  • Natural language processing (57.92%)
  • Machine translation (45.25%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (68.78%)
  • Natural language processing (57.92%)
  • Machine learning (7.69%)

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

Chris Callison-Burch mainly investigates Artificial intelligence, Natural language processing, Machine learning, Word and Language model. The various areas that Chris Callison-Burch examines in his Artificial intelligence study include Analogy and Set. A large part of his Natural language processing studies is devoted to Machine translation.

His Machine translation study combines topics in areas such as Pipeline and Space. His Machine learning study also includes

  • Test set most often made with reference to Inference,
  • Dialog box together with User interface, Transparency, Chatbot and Information retrieval. While the research belongs to areas of Word, he spends his time largely on the problem of Context, intersecting his research to questions surrounding Personal pronoun and Paraphrase.

Between 2018 and 2021, his most popular works were:

  • Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims. (42 citations)
  • Comparison of Diverse Decoding Methods from Conditional Language Models (34 citations)
  • Automatic Detection of Generated Text is Easiest when Humans are Fooled (25 citations)

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

  • Artificial intelligence
  • Natural language processing
  • Machine learning

Chris Callison-Burch mainly focuses on Artificial intelligence, Natural language processing, Machine learning, Sampling and Set. Chris Callison-Burch mostly deals with Language model in his studies of Artificial intelligence. The concepts of his Language model study are interwoven with issues in Hierarchical database model and Cluster analysis.

He has researched Natural language processing in several fields, including Social media, Word, Word meaning and Fluency. The Fluency study combines topics in areas such as Sentence, Content word, Rewriting and Reinforcement learning. His Machine learning study combines topics from a wide range of disciplines, such as Space, Decoding methods, Set and Natural 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

Moses: Open Source Toolkit for Statistical Machine Translation

Philipp Koehn;Hieu Hoang;Alexandra Birch;Chris Callison-Burch.
meeting of the association for computational linguistics (2007)

6096 Citations

Findings of the 2009 Workshop on Statistical Machine Translation

Chris Callison-Burch;Philipp Koehn;Christof Monz;Josh Schroeder.
workshop on statistical machine translation (2009)

775 Citations

Findings of the 2012 Workshop on Statistical Machine Translation

Chris Callison-Burch;Philipp Koehn;Christof Monz;Matt Post.
workshop on statistical machine translation (2012)

775 Citations

Findings of the 2011 Workshop on Statistical Machine Translation

Chris Callison-Burch;Philipp Koehn;Christof Monz;Omar Zaidan.
workshop on statistical machine translation (2011)

771 Citations

Re-evaluating the Role of Bleu in Machine Translation Research

Chris Callison-Burch;Miles Osborne;Philipp Koehn.
conference of the european chapter of the association for computational linguistics (2006)

751 Citations

PPDB: The Paraphrase Database

Juri Ganitkevitch;Benjamin Van Durme;Chris Callison-Burch.
north american chapter of the association for computational linguistics (2013)

707 Citations

Paraphrasing with Bilingual Parallel Corpora

Colin Bannard;Chris Callison-Burch.
meeting of the association for computational linguistics (2005)

698 Citations

Method and apparatus for providing multilingual translation over a network

Christopher Callison-Burch;Jeffrey Chin;Raymond Flournoy;Pria Hidisyan.
(2001)

617 Citations

Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon's Mechanical Turk

Chris Callison-Burch.
empirical methods in natural language processing (2009)

583 Citations

Findings of the 2013 Workshop on Statistical Machine Translation

Ondřej Bojar;Christian Buck;Chris Callison-Burch;Christian Federmann.
workshop on statistical machine translation (2013)

458 Citations

Best Scientists Citing Chris Callison-Burch

Andy Way

Andy Way

Dublin City University

Publications: 140

Philipp Koehn

Philipp Koehn

Johns Hopkins University

Publications: 93

Eiichiro Sumita

Eiichiro Sumita

National Institute of Information and Communications Technology

Publications: 89

Qun Liu

Qun Liu

Huawei Technologies (China)

Publications: 78

Lucia Specia

Lucia Specia

Imperial College London

Publications: 78

Graham Neubig

Graham Neubig

Carnegie Mellon University

Publications: 77

Francisco Casacuberta

Francisco Casacuberta

Universitat Politècnica de València

Publications: 73

Marcello Federico

Marcello Federico

Amazon (United States)

Publications: 63

Hermann Ney

Hermann Ney

RWTH Aachen University

Publications: 61

Nizar Habash

Nizar Habash

New York University Abu Dhabi

Publications: 58

Josef van Genabith

Josef van Genabith

German Research Centre for Artificial Intelligence

Publications: 57

Jörg Tiedemann

Jörg Tiedemann

University of Helsinki

Publications: 56

Stephan Vogel

Stephan Vogel

University of Graz

Publications: 55

Holger Schwenk

Holger Schwenk

Facebook (United States)

Publications: 55

Alex Waibel

Alex Waibel

Carnegie Mellon University

Publications: 55

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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