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 49 Citations 8,374 176 World Ranking 3046 National Ranking 1606

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Programming language
  • Machine learning

Benjamin Van Durme focuses on Artificial intelligence, Natural language processing, Information retrieval, Sentence and Paraphrase. Artificial intelligence is closely attributed to Machine learning in his study. His Natural language processing research includes elements of Annotation, Context, Logical consequence and Inference.

His studies deal with areas such as Class, Parsing and Knowledge extraction as well as Information retrieval. In Sentence, he works on issues like Structure, which are connected to Representation and Construct. His Paraphrase study combines topics from a wide range of disciplines, such as Syntax, Variety and Set.

His most cited work include:

  • PPDB: The Paraphrase Database (565 citations)
  • Information Extraction over Structured Data: Question Answering with Freebase (340 citations)
  • Hypothesis Only Baselines in Natural Language Inference (266 citations)

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

His primary areas of investigation include Artificial intelligence, Natural language processing, Sentence, Machine learning and Information retrieval. While the research belongs to areas of Artificial intelligence, Benjamin Van Durme spends his time largely on the problem of Structure, intersecting his research to questions surrounding Pipeline. His work is dedicated to discovering how Natural language processing, Inference are connected with Logical consequence and other disciplines.

His Sentence study incorporates themes from Language model, Context and Cluster analysis. His work deals with themes such as Annotation and Knowledge extraction, which intersect with Information retrieval. His research integrates issues of Text corpus and Construct in his study of Natural language.

He most often published in these fields:

  • Artificial intelligence (68.42%)
  • Natural language processing (56.28%)
  • Sentence (14.57%)

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

  • Artificial intelligence (68.42%)
  • Natural language processing (56.28%)
  • Coreference (6.88%)

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

His main research concerns Artificial intelligence, Natural language processing, Coreference, Natural language and Sentence. His work on Semantics as part of general Artificial intelligence study is frequently connected to Intuition, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. Many of his research projects under Natural language processing are closely connected to Tax law with Tax law, tying the diverse disciplines of science together.

The study incorporates disciplines such as Language model, Text corpus and Prolog in addition to Natural language. His work deals with themes such as Argument, Representation, Reduction, Hash function and Similarity, which intersect with Sentence. Benjamin Van Durme has included themes like Rule mining and Information retrieval in his Structure study.

Between 2019 and 2021, his most popular works were:

  • Uncertain Natural Language Inference (14 citations)
  • Which *BERT? A Survey Organizing Contextualized Encoders (10 citations)
  • Multi-Sentence Argument Linking. (9 citations)

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

  • Artificial intelligence
  • Programming language
  • Machine learning

Artificial intelligence, Natural language processing, Natural language, Word and Text corpus are his primary areas of study. His Artificial intelligence research includes elements of Structure and Causal inference. Benjamin Van Durme combines subjects such as Semantics and Event with his study of Natural language processing.

His Event study deals with Semantic role labeling intersecting with Coreference and Resolution. His study looks at the relationship between Word and fields such as Language model, as well as how they intersect with chemical problems. His Text corpus research includes themes of Question answering, Logical consequence and Natural language understanding.

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

PPDB: The Paraphrase Database

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

707 Citations

Information Extraction over Structured Data: Question Answering with Freebase

Xuchen Yao;Benjamin Van Durme.
meeting of the association for computational linguistics (2014)

444 Citations

Annotated Gigaword

Courtney Napoles;Matthew Gormley;Benjamin Van Durme.
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX) (2012)

296 Citations

Hypothesis Only Baselines in Natural Language Inference

Adam Poliak;Jason Naradowsky;Aparajita Haldar;Rachel Rudinger.
joint conference on lexical and computational semantics (2018)

266 Citations

PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification

Ellie Pavlick;Pushpendre Rastogi;Juri Ganitkevitch;Benjamin Van Durme.
international joint conference on natural language processing (2015)

248 Citations

Gender Bias in Coreference Resolution

Rachel Rudinger;Jason Naradowsky;Brian Leonard;Benjamin Van Durme.
north american chapter of the association for computational linguistics (2018)

218 Citations

Answer Extraction as Sequence Tagging with Tree Edit Distance

Xuchen Yao;Benjamin Van Durme;Chris Callison-Burch;Peter Clark.
north american chapter of the association for computational linguistics (2013)

213 Citations

What do you learn from context? Probing for sentence structure in contextualized word representations

Ian Tenney;Patrick Xia;Berlin Chen;Alex Wang.
arXiv: Computation and Language (2019)

211 Citations

What you seek is what you get: extraction of class attributes from query logs

Marius Pasca;Benjamin Van Durme.
international joint conference on artificial intelligence (2007)

176 Citations

ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension.

Sheng Zhang;Xiaodong Liu;Jingjing Liu;Jianfeng Gao.
arXiv: Computation and Language (2018)

172 Citations

Best Scientists Citing Benjamin Van Durme

Yejin Choi

Yejin Choi

Allen Institute for Artificial Intelligence

Publications: 47

Dan Roth

Dan Roth

University of Pennsylvania

Publications: 43

Iryna Gurevych

Iryna Gurevych

University of Paderborn

Publications: 37

Chris Callison-Burch

Chris Callison-Burch

University of Pennsylvania

Publications: 36

Hinrich Schütze

Hinrich Schütze

Ludwig-Maximilians-Universität München

Publications: 35

Samuel R. Bowman

Samuel R. Bowman

New York University

Publications: 34

Eduard Hovy

Eduard Hovy

Carnegie Mellon University

Publications: 34

Kevin Gimpel

Kevin Gimpel

Toyota Technological Institute at Chicago

Publications: 32

Mohit Bansal

Mohit Bansal

University of North Carolina at Chapel Hill

Publications: 32

Kai-Wei Chang

Kai-Wei Chang

University of California, Los Angeles

Publications: 32

Graham Neubig

Graham Neubig

Carnegie Mellon University

Publications: 31

Noah A. Smith

Noah A. Smith

University of Washington

Publications: 31

Mirella Lapata

Mirella Lapata

University of Edinburgh

Publications: 29

Bing Liu

Bing Liu

Peking University

Publications: 27

Karen Livescu

Karen Livescu

Toyota Technological Institute at Chicago

Publications: 25

Ido Dagan

Ido Dagan

Bar-Ilan University

Publications: 25

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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