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 53 Citations 9,270 243 World Ranking 2484 National Ranking 1332

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Natural language processing

His primary areas of study are Artificial intelligence, Natural language processing, Machine learning, Entity linking and Knowledge base. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Event, Contrast and Data mining. His Natural language processing research is multidisciplinary, incorporating elements of Space, Word and Database.

His biological study spans a wide range of topics, including Scheme, Layer and Pipeline. When carried out as part of a general Entity linking research project, his work on Weak entity is frequently linked to work in Population, therefore connecting diverse disciplines of study. Heng Ji focuses mostly in the field of Surprise, narrowing it down to topics relating to Annotation and, in certain cases, Visualization.

His most cited work include:

  • Overview of the TAC 2010 Knowledge Base Population Track (324 citations)
  • Overview of the TAC 2010 Knowledge Base Population Track (324 citations)
  • Joint Event Extraction via Structured Prediction with Global Features (302 citations)

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

The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Information retrieval, Information extraction and Knowledge base. His Artificial intelligence research is multidisciplinary, relying on both Event and Machine learning. His study in Natural language processing is interdisciplinary in nature, drawing from both Domain, Speech recognition and Coreference.

His work in the fields of Information retrieval, such as Ranking, overlaps with other areas such as Cross media. His work carried out in the field of Information extraction brings together such families of science as Pipeline, Data mining, Inference and Data science. His research integrates issues of Context and Knowledge extraction in his study of Knowledge base.

He most often published in these fields:

  • Artificial intelligence (58.90%)
  • Natural language processing (45.89%)
  • Information retrieval (22.95%)

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

  • Artificial intelligence (58.90%)
  • Natural language processing (45.89%)
  • Benchmark (6.51%)

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

His main research concerns Artificial intelligence, Natural language processing, Benchmark, Event and Annotation. Heng Ji regularly links together related areas like Machine learning in his Artificial intelligence studies. The Natural language processing study combines topics in areas such as Domain and Word, Word embedding.

His Benchmark research includes themes of Speech translation, Machine translation, Space, Embedding and Consistency. The concepts of his Event study are interwoven with issues in Argument and Coreference. Heng Ji works mostly in the field of Relationship extraction, limiting it down to concerns involving Graph and, occasionally, Information retrieval.

Between 2018 and 2021, his most popular works were:

  • The Age of Social Sensing (80 citations)
  • Joint Entity and Event Extraction with Generative Adversarial Imitation Learning (35 citations)
  • Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization (32 citations)

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

  • Artificial intelligence
  • Machine learning
  • Programming language

Heng Ji mostly deals with Artificial intelligence, Natural language processing, Graph, Event and Information retrieval. His study involves Information extraction, Recurrent neural network, Edit distance, Encoding and Cluster analysis, a branch of Artificial intelligence. His Information extraction study integrates concerns from other disciplines, such as Generative adversarial network, Sentence, Imitation, Pairwise comparison and Machine learning.

His work on Relationship extraction as part of general Natural language processing research is frequently linked to Heuristics, bridging the gap between disciplines. His Event research includes elements of Adversarial system, Annotation, Space, Argument and Imitation learning. Heng Ji interconnects Simple and Turing in the investigation of issues within Information retrieval.

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

Overview of the TAC 2010 Knowledge Base Population Track

Heng Ji;Heng Ji;Ralph Grishman;Hoa Trang Dang;Kira Griffitt.
(2010)

442 Citations

Refining Event Extraction through Cross-Document Inference

Heng Ji;Ralph Grishman.
meeting of the association for computational linguistics (2008)

389 Citations

Joint Event Extraction via Structured Prediction with Global Features

Qi Li;Heng Ji;Liang Huang.
meeting of the association for computational linguistics (2013)

349 Citations

Knowledge Base Population: Successful Approaches and Challenges

Heng Ji;Ralph Grishman.
meeting of the association for computational linguistics (2011)

347 Citations

Incremental Joint Extraction of Entity Mentions and Relations

Qi Li;Heng Ji.
meeting of the association for computational linguistics (2014)

293 Citations

A Language-Independent Neural Network for Event Detection.

Xiaocheng Feng;Lifu Huang;Duyu Tang;Heng Ji.
meeting of the association for computational linguistics (2016)

184 Citations

FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation

Fenglong Ma;Yaliang Li;Qi Li;Minghui Qiu.
knowledge discovery and data mining (2015)

166 Citations

CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases

Xiang Ren;Zeqiu Wu;Wenqi He;Meng Qu.
the web conference (2017)

165 Citations

A Dependency-Based Neural Network for Relation Classification

Yang Liu;Furu Wei;Sujian Li;Heng Ji.
international joint conference on natural language processing (2015)

164 Citations

Exploring Context and Content Links in Social Media: A Latent Space Method

Guo-Jun Qi;C. Aggarwal;Qi Tian;Heng Ji.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)

137 Citations

Best Scientists Citing Heng Ji

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 58

Dan Roth

Dan Roth

University of Pennsylvania

Publications: 53

Xiang Ren

Xiang Ren

University of Southern California

Publications: 43

Ralph Grishman

Ralph Grishman

New York University

Publications: 37

Juanzi Li

Juanzi Li

Tsinghua University

Publications: 29

Benjamin Van Durme

Benjamin Van Durme

Johns Hopkins University

Publications: 28

Yue Zhang

Yue Zhang

Westlake University

Publications: 27

Kang Liu

Kang Liu

Chinese Academy of Sciences

Publications: 26

Jun Zhao

Jun Zhao

Chinese Academy of Sciences

Publications: 26

Yangqiu Song

Yangqiu Song

Hong Kong University of Science and Technology

Publications: 25

Arkaitz Zubiaga

Arkaitz Zubiaga

Queen Mary University of London

Publications: 25

Ting Liu

Ting Liu

Harbin Institute of Technology

Publications: 25

Jing Gao

Jing Gao

Purdue University West Lafayette

Publications: 25

Eduard Hovy

Eduard Hovy

Carnegie Mellon University

Publications: 24

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 22

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.

If you think any of the details on this page are incorrect, let us know.

Contact us
Something went wrong. Please try again later.