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 37 Citations 8,664 117 World Ranking 5314 National Ranking 2599

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Linguistics
  • Machine learning

His primary scientific interests are in Artificial intelligence, Natural language processing, Topic model, Machine learning and Inference. His study deals with a combination of Artificial intelligence and Matching. While the research belongs to areas of Natural language processing, he spends his time largely on the problem of Artificial neural network, intersecting his research to questions surrounding Principle of compositionality, Question answering and Interpersonal communication.

His Topic model study integrates concerns from other disciplines, such as Discrete mathematics, Class, Formalism, Parallel corpora and Statistical model. In general Machine learning, his work in Document clustering is often linked to Yield, Depression and Conceptual clustering linking many areas of study. In his research, Markov chain is intimately related to Latent Dirichlet allocation, which falls under the overarching field of Inference.

His most cited work include:

  • Reading Tea Leaves: How Humans Interpret Topic Models (1214 citations)
  • Deep Unordered Composition Rivals Syntactic Methods for Text Classification (474 citations)
  • A Neural Network for Factoid Question Answering over Paragraphs (266 citations)

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

His primary areas of study are Artificial intelligence, Natural language processing, Topic model, Machine learning and Question answering. Jordan Boyd-Graber studied Artificial intelligence and Pattern recognition that intersect with Feature. His research brings together the fields of Annotation and Natural language processing.

He interconnects Quality, Prior probability and Data science in the investigation of issues within Topic model. His Question answering research includes themes of Adversarial system, Context, Natural language and Ambiguity. His work in Word tackles topics such as Embedding which are related to areas like Similarity.

He most often published in these fields:

  • Artificial intelligence (59.39%)
  • Natural language processing (34.55%)
  • Topic model (30.91%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (59.39%)
  • Natural language processing (34.55%)
  • Question answering (15.15%)

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

His main research concerns Artificial intelligence, Natural language processing, Question answering, Data science and Word. His Artificial intelligence study frequently links to other fields, such as SQL. His Machine translation study in the realm of Natural language processing interacts with subjects such as Lexico.

Jordan Boyd-Graber focuses mostly in the field of Question answering, narrowing it down to matters related to Ambiguity and, in some cases, Variation and Subject. His study in Data science is interdisciplinary in nature, drawing from both The Internet and Natural language. His work carried out in the field of Word brings together such families of science as Embedding, Document classification, Representation, Similarity and Representation.

Between 2019 and 2021, his most popular works were:

  • No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML (16 citations)
  • NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned (8 citations)
  • Interactive Refinement of Cross-Lingual Word Embeddings (8 citations)

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

  • Artificial intelligence
  • Linguistics
  • Machine learning

Jordan Boyd-Graber mainly focuses on Natural language processing, Word, Artificial intelligence, Data science and Question answering. His study in the fields of Document classification under the domain of Natural language processing overlaps with other disciplines such as Knowledge transfer. Jordan Boyd-Graber has researched Word in several fields, including Active learning, Similarity, Overfitting, Similarity and Character.

His study ties his expertise on Space together with the subject of Artificial intelligence. His studies in Data science integrate themes in fields like The Internet and Natural language. His Factoid study in the realm of Question answering connects with subjects such as Text messaging.

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

Reading Tea Leaves: How Humans Interpret Topic Models

Jonathan Chang;Sean Gerrish;Chong Wang;Jordan L. Boyd-graber.
neural information processing systems (2009)

1906 Citations

Deep Unordered Composition Rivals Syntactic Methods for Text Classification

Mohit Iyyer;Varun Manjunatha;Jordan Boyd-Graber;Hal Daumé Iii.
international joint conference on natural language processing (2015)

684 Citations

A Neural Network for Factoid Question Answering over Paragraphs

Mohit Iyyer;Jordan Boyd-Graber;Leonardo Claudino;Richard Socher.
empirical methods in natural language processing (2014)

388 Citations

Interactive Topic Modeling

Yuening Hu;Jordan Boyd-Graber;Brianna Satinoff.
meeting of the association for computational linguistics (2011)

327 Citations

A Topic Model for Word Sense Disambiguation

Jordan Boyd-Graber;David Blei;Xiaojin Zhu.
empirical methods in natural language processing (2007)

308 Citations

Syntactic Topic Models

Jordan L. Boyd-graber;David M. Blei.
neural information processing systems (2008)

252 Citations

Political Ideology Detection Using Recursive Neural Networks

Mohit Iyyer;Peter Enns;Jordan Boyd-Graber;Philip Resnik.
meeting of the association for computational linguistics (2014)

246 Citations

Mr. LDA: a flexible large scale topic modeling package using variational inference in MapReduce

Ke Zhai;Jordan Boyd-Graber;Nima Asadi;Mohamad L. Alkhouja.
the web conference (2012)

196 Citations

Connections between the lines: augmenting social networks with text

Jonathan Chang;Jordan Boyd-Graber;David M. Blei.
knowledge discovery and data mining (2009)

177 Citations

Applications of Topic Models

Jordan L. Boyd-Graber;Yuening Hu;David M. Mimno.
(2017)

175 Citations

Best Scientists Citing Jordan Boyd-Graber

Noah A. Smith

Noah A. Smith

University of Washington

Publications: 36

Bing Liu

Bing Liu

Peking University

Publications: 28

David M. Blei

David M. Blei

Columbia University

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Ivan Vulić

Ivan Vulić

University of Cambridge

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Timothy Baldwin

Timothy Baldwin

University of Melbourne

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Mirella Lapata

Mirella Lapata

University of Edinburgh

Publications: 18

Sebastian Riedel

Sebastian Riedel

University College London

Publications: 18

Benjamin Van Durme

Benjamin Van Durme

Johns Hopkins University

Publications: 18

Eric P. Xing

Eric P. Xing

Carnegie Mellon University

Publications: 17

Wray Buntine

Wray Buntine

Monash University

Publications: 17

Philipp Koehn

Philipp Koehn

Johns Hopkins University

Publications: 16

Liang Huang

Liang Huang

Baidu (China)

Publications: 16

Graham Neubig

Graham Neubig

Carnegie Mellon University

Publications: 15

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

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Byron C. Wallace

Byron C. Wallace

Northeastern University

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Sinno Jialin Pan

Sinno Jialin Pan

Nanyang Technological University

Publications: 15

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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