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.
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