What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Data mining, Information retrieval and Natural language processing.
His multidisciplinary approach integrates Artificial intelligence and Component in his work.
His studies deal with areas such as Legal expert system and Fuzzy control system as well as Machine learning.
The concepts of his Data mining study are interwoven with issues in Textual information, Document classification, Unsupervised learning and Trading strategy.
In his study, Text graph is strongly linked to World Wide Web, which falls under the umbrella field of Information retrieval.
His Automatic summarization and Topic model study, which is part of a larger body of work in Natural language processing, is frequently linked to Pronunciation, bridging the gap between disciplines.
His most cited work include:
- LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE (709 citations)
- MEAD - A Platform for Multidocument Multilingual Text Summarization (304 citations)
- A multilevel approach to intelligent information filtering: model, system, and evaluation (186 citations)
What are the main themes of his work throughout his whole career to date?
Wai Lam spends much of his time researching Artificial intelligence, Information retrieval, Machine learning, Data mining and Natural language processing.
When carried out as part of a general Artificial intelligence research project, his work on Sentence, Bayesian network and Automatic summarization is frequently linked to work in Component, therefore connecting diverse disciplines of study.
The study incorporates disciplines such as Ranking, World Wide Web, Categorization and Document clustering in addition to Information retrieval.
His Machine learning research focuses on Benchmark and how it connects with Sentiment analysis.
His studies in Data mining integrate themes in fields like Graphical model, Information extraction and Web page.
His study in Graphical model is interdisciplinary in nature, drawing from both Probabilistic logic, Discriminative model, Inference and Conditional random field.
He most often published in these fields:
- Artificial intelligence (56.98%)
- Information retrieval (34.11%)
- Machine learning (28.68%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (56.98%)
- Information retrieval (34.11%)
- Natural language processing (18.99%)
In recent papers he was focusing on the following fields of study:
Wai Lam focuses on Artificial intelligence, Information retrieval, Natural language processing, Question answering and Benchmark.
His Artificial intelligence study frequently draws connections to adjacent fields such as Machine learning.
In the field of Information retrieval, his study on Snippet overlaps with subjects such as Component.
His biological study spans a wide range of topics, including E-commerce, Ranking, Relevance and Data science.
His Benchmark study integrates concerns from other disciplines, such as Sentiment analysis, Smoothing, Autoencoder, Knowledge base and Relation.
His Sentence research is multidisciplinary, relying on both Semantics, Word and Lexicon.
Between 2017 and 2021, his most popular works were:
- Transformation Networks for Target-Oriented Sentiment Classification (148 citations)
- A Unified Model for Opinion Target Extraction and Target Sentiment Prediction (73 citations)
- Aspect Term Extraction with History Attention and Selective Transformation (63 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
Wai Lam spends much of his time researching Artificial intelligence, Information retrieval, Sentence, Benchmark and Sentiment analysis.
In general Artificial intelligence study, his work on Automatic summarization often relates to the realm of Quality, thereby connecting several areas of interest.
His study explores the link between Information retrieval and topics such as Training set that cross with problems in Generative model.
He interconnects Semantics and Word in the investigation of issues within Sentence.
His biological study deals with issues like Machine learning, which deal with fields such as Language model and End-to-end principle.
His Sentiment analysis research incorporates themes from Dependency and Relation, Data mining.
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