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 56 Citations 16,487 152 World Ranking 1994 National Ranking 1090

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Operating system
  • Machine learning

Yejin Choi mainly investigates Artificial intelligence, Natural language processing, Context, Natural language and Machine learning. His research combines Quality and Artificial intelligence. His research integrates issues of Checklist, Image and Internet privacy in his study of Natural language processing.

His Context research is multidisciplinary, incorporating elements of Question answering, News media, Dialog box and Reading comprehension. His study in Machine learning is interdisciplinary in nature, drawing from both Context-free grammar and Stylometry. His Language model study incorporates themes from Counterfactual conditional, Commonsense knowledge and Explicit knowledge.

His most cited work include:

  • BabyTalk: Understanding and Generating Simple Image Descriptions (489 citations)
  • Finding Deceptive Opinion Spam by Any Stretch of the Imagination (431 citations)
  • OpinionFinder: A System for Subjectivity Analysis (416 citations)

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

His scientific interests lie mostly in Artificial intelligence, Natural language processing, Language model, Commonsense reasoning and Machine learning. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Context and Set. His research in Natural language processing intersects with topics in Image and Reading comprehension.

His work in Language model tackles topics such as Commonsense knowledge which are related to areas like Knowledge graph. The various areas that Yejin Choi examines in his Commonsense reasoning study include Crowdsourcing, Frame, Cognitive science and Human–computer interaction. His work deals with themes such as Adversarial system and Benchmark, which intersect with Machine learning.

He most often published in these fields:

  • Artificial intelligence (65.00%)
  • Natural language processing (40.00%)
  • Language model (29.17%)

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

  • Artificial intelligence (65.00%)
  • Language model (29.17%)
  • Natural language processing (40.00%)

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

Artificial intelligence, Language model, Natural language processing, Commonsense reasoning and Machine learning are his primary areas of study. His work in the fields of Artificial intelligence, such as Natural language, overlaps with other areas such as Transformer. His Language model research includes elements of Transfer of learning, Decoding methods, Commonsense knowledge, Range and Natural language generation.

His Natural language processing research is multidisciplinary, relying on both Narrative, Interpretability, Image and Scripting language. The Commonsense reasoning study combines topics in areas such as Inference, Question answering, Frame, Cognitive science and Generative grammar. When carried out as part of a general Machine learning research project, his work on Overfitting and Leverage is frequently linked to work in Data mapping and Degeneration, therefore connecting diverse disciplines of study.

Between 2019 and 2021, his most popular works were:

  • The Curious Case of Neural Text Degeneration (348 citations)
  • WinoGrande: An Adversarial Winograd Schema Challenge at Scale (104 citations)
  • Abductive Commonsense Reasoning (83 citations)

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

  • Artificial intelligence
  • Machine learning
  • Operating system

Yejin Choi spends much of his time researching Artificial intelligence, Commonsense reasoning, Language model, Machine learning and Natural language processing. His Statistical model and Word study are his primary interests in Artificial intelligence. His Commonsense reasoning study combines topics in areas such as Inference, Question answering, Crowdsourcing, Generative grammar and Natural language.

The study incorporates disciplines such as Text corpus, Commonsense knowledge, Winograd Schema Challenge, Benchmark and Transfer of learning in addition to Language model. His work on Overfitting and Leverage is typically connected to Data mapping and Degeneration as part of general Machine learning study, connecting several disciplines of science. His studies deal with areas such as Object, Semantics, Image and Set as well as Natural language processing.

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

BabyTalk: Understanding and Generating Simple Image Descriptions

Girish Kulkarni;Visruth Premraj;Vicente Ordonez;Sagnik Dhar.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)

1164 Citations

OpinionFinder: A System for Subjectivity Analysis

Theresa Wilson;Paul Hoffmann;Swapna Somasundaran;Jason Kessler.
empirical methods in natural language processing (2005)

656 Citations

Baby talk: Understanding and generating simple image descriptions

Girish Kulkarni;Visruth Premraj;Sagnik Dhar;Siming Li.
computer vision and pattern recognition (2011)

628 Citations

Finding Deceptive Opinion Spam by Any Stretch of the Imagination

Myle Ott;Yejin Choi;Claire Cardie;Jeffrey T. Hancock.
meeting of the association for computational linguistics (2011)

616 Citations

Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns

Yejin Choi;Claire Cardie;Ellen Riloff;Siddharth Patwardhan.
empirical methods in natural language processing (2005)

472 Citations

Syntactic Stylometry for Deception Detection

Song Feng;Ritwik Banerjee;Yejin Choi.
meeting of the association for computational linguistics (2012)

456 Citations

Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis

Yejin Choi;Claire Cardie.
empirical methods in natural language processing (2008)

400 Citations

Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking

Hannah Rashkin;Eunsol Choi;Jin Yea Jang;Svitlana Volkova.
empirical methods in natural language processing (2017)

380 Citations

Composing Simple Image Descriptions using Web-scale N-grams

Siming Li;Girish Kulkarni;Tamara L. Berg;Alexander C. Berg.
conference on computational natural language learning (2011)

351 Citations

The Curious Case of Neural Text Degeneration

Ari Holtzman;Jan Buys;Leo Du;Maxwell Forbes.
international conference on learning representations (2020)

348 Citations

Best Scientists Citing Yejin Choi

Bing Liu

Bing Liu

Peking University

Publications: 64

Jianfeng Gao

Jianfeng Gao

Microsoft (United States)

Publications: 59

Claire Cardie

Claire Cardie

Cornell University

Publications: 49

Devi Parikh

Devi Parikh

Facebook (United States)

Publications: 47

Rada Mihalcea

Rada Mihalcea

University of Michigan–Ann Arbor

Publications: 43

Mohit Bansal

Mohit Bansal

University of North Carolina at Chapel Hill

Publications: 42

Preslav Nakov

Preslav Nakov

Qatar Computing Research Institute

Publications: 42

Dan Roth

Dan Roth

University of Pennsylvania

Publications: 42

Benjamin Van Durme

Benjamin Van Durme

Johns Hopkins University

Publications: 41

Noah A. Smith

Noah A. Smith

University of Washington

Publications: 40

Marcus Rohrbach

Marcus Rohrbach

Facebook (United States)

Publications: 36

Xiang Ren

Xiang Ren

University of Southern California

Publications: 35

Mirella Lapata

Mirella Lapata

University of Edinburgh

Publications: 35

William Yang Wang

William Yang Wang

University of California, Santa Barbara

Publications: 34

Ting Liu

Ting Liu

Harbin Institute of Technology

Publications: 33

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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