What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Machine learning
Liu Wenyin spends much of his time researching Information retrieval, Artificial intelligence, Image retrieval, Web page and Phishing.
His work deals with themes such as Similarity, World Wide Web and Web modeling, which intersect with Information retrieval.
The study incorporates disciplines such as Natural language processing, Machine learning, Computer vision and Pattern recognition in addition to Artificial intelligence.
He has included themes like Social media and Microblogging in his Natural language processing study.
His studies examine the connections between Web page and genetics, as well as such issues in Ranking, with regards to Cluster analysis, Identification and Set.
Normalization, Object detection, Ground truth, Search engine indexing and Text detection is closely connected to Data mining in his research, which is encompassed under the umbrella topic of Phishing.
His most cited work include:
- Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD) (231 citations)
- Statistical bigram correlation model for image retrieval (195 citations)
- Semi-Automatic Image Annotation. (154 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Information retrieval, World Wide Web, Graphics and Question answering.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Pattern recognition, Computer vision and Natural language processing.
His Information retrieval research incorporates elements of Similarity, Web page and Image retrieval, Relevance feedback.
He has researched World Wide Web in several fields, including Multimedia, User modeling and Association.
His Graphics study integrates concerns from other disciplines, such as Algorithm, Computer graphics and Pattern recognition.
His Question answering research focuses on Web application and how it relates to Semantics.
He most often published in these fields:
- Artificial intelligence (35.37%)
- Information retrieval (32.93%)
- World Wide Web (17.07%)
What were the highlights of his more recent work (between 2010-2015)?
- Artificial intelligence (35.37%)
- Information retrieval (32.93%)
- Natural language processing (14.02%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Information retrieval, Natural language processing, Question answering and Sentiment analysis.
His Artificial intelligence research incorporates themes from Machine learning, Metric and Pattern recognition.
His research in Pattern recognition intersects with topics in Image noise, Non-local means, Noise reduction and Noise.
In his works, he undertakes multidisciplinary study on Information retrieval and Weighting.
His study looks at the relationship between Natural language processing and topics such as Ambiguity, which overlap with Structure.
His Question answering research includes elements of Pattern matching, Categorization, Similarity, Semantic computing and Semantics.
Between 2010 and 2015, his most popular works were:
- Sentiment topic models for social emotion mining (105 citations)
- Boosting weighted ELM for imbalanced learning (100 citations)
- Building emotional dictionary for sentiment analysis of online news (94 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Machine learning
Liu Wenyin focuses on Artificial intelligence, Natural language processing, Information retrieval, Microblogging and Social media.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.
Liu Wenyin works in the field of Natural language processing, focusing on Lexicon in particular.
In the field of Information retrieval, his study on Snippet overlaps with subjects such as Term.
His biological study spans a wide range of topics, including Sentiment analysis, Multimedia and Topic model.
He combines subjects such as Data science and Affective science, Emotion classification with his study of Social media.
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