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 34 Citations 6,645 220 World Ranking 6224 National Ranking 589

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Programming language

His scientific interests lie mostly in Artificial intelligence, Natural language processing, Rumor, Machine learning and Social media. His research in Artificial intelligence is mostly focused on Sentence. His research integrates issues of Polarity, Word and Information retrieval in his study of Natural language processing.

The Artificial neural network and Recurrent neural network research Kam-Fai Wong does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Logical relations, therefore creating a link between diverse domains of science. His study in the fields of Microblogging under the domain of Social media overlaps with other disciplines such as Tree kernel. Kam-Fai Wong usually deals with Microblogging and limits it to topics linked to Variation and World Wide Web.

His most cited work include:

  • Interpreting TF-IDF term weights as making relevance decisions (508 citations)
  • Detecting rumors from microblogs with recurrent neural networks (388 citations)
  • Detect Rumors Using Time Series of Social Context Information on Microblogging Websites (255 citations)

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

His main research concerns Artificial intelligence, Natural language processing, Information retrieval, Data mining and Social media. His research in Artificial intelligence intersects with topics in Machine learning and Task. His Natural language processing research incorporates elements of Annotation, Speech recognition and Word.

His study brings together the fields of World Wide Web and Information retrieval. His biological study spans a wide range of topics, including Topic model, Conversation and Rumor. His studies in Relevance integrate themes in fields like Probabilistic logic and Vector space model.

He most often published in these fields:

  • Artificial intelligence (46.13%)
  • Natural language processing (33.45%)
  • Information retrieval (26.76%)

What were the highlights of his more recent work (between 2014-2020)?

  • Artificial intelligence (46.13%)
  • Social media (11.97%)
  • Natural language processing (33.45%)

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

Kam-Fai Wong spends much of his time researching Artificial intelligence, Social media, Natural language processing, Microblogging and Conversation. His biological study spans a wide range of topics, including Domain, Machine learning and Task. The various areas that he examines in his Social media study include Event, Rumor, Data mining and Internet privacy.

His work carried out in the field of Rumor brings together such families of science as Variation and Data science. The concepts of his Natural language processing study are interwoven with issues in Word, SemEval and Identification. His Microblogging study combines topics from a wide range of disciplines, such as Topic model, Information retrieval, Automatic summarization and Joint.

Between 2014 and 2020, his most popular works were:

  • Detecting rumors from microblogs with recurrent neural networks (388 citations)
  • Detect Rumors Using Time Series of Social Context Information on Microblogging Websites (255 citations)
  • Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning (160 citations)

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

  • Artificial intelligence
  • Machine learning
  • Programming language

The scientist’s investigation covers issues in Artificial intelligence, Task, Machine learning, Rumor and Social media. The study incorporates disciplines such as Conversation and Natural language processing in addition to Artificial intelligence. His study in the field of Task completion is also linked to topics like Policy learning.

His study on Artificial neural network, Recurrent neural network and Kernel is often connected to Tree kernel as part of broader study in Machine learning. His Rumor research also works with subjects such as

  • Variation together with Information retrieval,
  • Data science and related Order, Scheme and Multi-task learning. Particularly relevant to Microblogging is his body of work in Social media.

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

Interpreting TF-IDF term weights as making relevance decisions

Ho Chung Wu;Robert Wing Pong Luk;Kam Fai Wong;Kui Lam Kwok.
ACM Transactions on Information Systems (2008)

811 Citations

Detecting rumors from microblogs with recurrent neural networks

Jing Ma;Wei Gao;Prasenjit Mitra;Sejeong Kwon.
international joint conference on artificial intelligence (2016)

503 Citations

Detect Rumors Using Time Series of Social Context Information on Microblogging Websites

Jing Ma;Wei Gao;Zhongyu Wei;Yueming Lu.
conference on information and knowledge management (2015)

337 Citations

Component-based software engineering: technologies, development frameworks, and quality assurance schemes

Xia Cai;M.R. Lyu;Kam-Fai Wong;Roy Ko.
asia pacific software engineering conference (2000)

247 Citations

Extractive Summarization Using Supervised and Semi-Supervised Learning

Kam-Fai Wong;Mingli Wu;Wenjie Li.
international conference on computational linguistics (2008)

235 Citations

Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

Jing Ma;Wei Gao;Kam Fai Wong.
meeting of the association for computational linguistics (2017)

193 Citations

Rumor Detection on Twitter with Tree-structured Recursive Neural Networks

Jing Ma;Wei Gao;Kam-Fai Wong.
meeting of the association for computational linguistics (2018)

165 Citations

Web 2.0 environmental scanning and adaptive decision support for business mergers and acquisitions

Raymond Y. K. Lau;Stephen S. Y. Liao;K. F. Wong;Dickson K. W. Chiu.
Management Information Systems Quarterly (2012)

162 Citations

A genetic algorithm-based clustering approach for database partitioning

Chun-Hung Cheng;Wing-Kin Lee;Kam-Fai Wong.
systems man and cybernetics (2002)

154 Citations

A TSP-based heuristic for forming machine groups and part families

C. H. Cheng;Y.P. Gupta;W.H. Lee;K.F. Wong.
International Journal of Production Research (1998)

145 Citations

Best Scientists Citing Kam-Fai Wong

Preslav Nakov

Preslav Nakov

Qatar Computing Research Institute

Publications: 44

Jianfeng Gao

Jianfeng Gao

Microsoft (United States)

Publications: 29

Minlie Huang

Minlie Huang

Tsinghua University

Publications: 25

Bing Liu

Bing Liu

Peking University

Publications: 24

Xiaojun Wan

Xiaojun Wan

Peking University

Publications: 18

Raymond Y. K. Lau

Raymond Y. K. Lau

City University of Hong Kong

Publications: 16

Alberto Barrón-Cedeño

Alberto Barrón-Cedeño

University of Bologna

Publications: 16

Yulan He

Yulan He

University of Warwick

Publications: 16

Huan Liu

Huan Liu

Arizona State University

Publications: 15

Maria Liakata

Maria Liakata

Turing Institute

Publications: 15

Michael R. Lyu

Michael R. Lyu

Chinese University of Hong Kong

Publications: 15

Arkaitz Zubiaga

Arkaitz Zubiaga

Queen Mary University of London

Publications: 14

Erik Cambria

Erik Cambria

Nanyang Technological University

Publications: 14

James Glass

James Glass

MIT

Publications: 13

Lluís Màrquez

Lluís Màrquez

Amazon (United States)

Publications: 12

Pushpak Bhattacharyya

Pushpak Bhattacharyya

Indian Institute of Technology Patna

Publications: 12

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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