What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Information retrieval, Artificial intelligence, Language model, Data mining and Machine learning.
His study looks at the relationship between Information retrieval and fields such as Divergence-from-randomness model, as well as how they intersect with chemical problems.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Heuristics and Natural language processing.
His research integrates issues of Smoothing, Ranking, Relevance, Heuristic and Component in his study of Language model.
In his study, Redundancy and Baseline is strongly linked to Mixture model, which falls under the umbrella field of Data mining.
His study in Machine learning is interdisciplinary in nature, drawing from both Iterative method, Divergence, Adaptation, Domain adaptation and Perspective.
His most cited work include:
- A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval (1533 citations)
- A study of smoothing methods for language models applied to information retrieval (1098 citations)
- Document Language Models, Query Models, and Risk Minimization for Information Retrieval (783 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Information retrieval, Artificial intelligence, Data mining, Natural language processing and Machine learning.
ChengXiang Zhai regularly ties together related areas like Language model in his Information retrieval studies.
His Language model study integrates concerns from other disciplines, such as Smoothing and Divergence-from-randomness model.
The study incorporates disciplines such as Text mining, Relevance and Pattern recognition in addition to Artificial intelligence.
His Data mining research is multidisciplinary, incorporating elements of Mixture model, Set and Cluster analysis.
ChengXiang Zhai works mostly in the field of Probabilistic logic, limiting it down to topics relating to Topic model and, in certain cases, Data science.
He most often published in these fields:
- Information retrieval (48.86%)
- Artificial intelligence (33.33%)
- Data mining (17.35%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (33.33%)
- Information retrieval (48.86%)
- Machine learning (15.98%)
In recent papers he was focusing on the following fields of study:
ChengXiang Zhai mainly investigates Artificial intelligence, Information retrieval, Machine learning, Data science and Natural language processing.
His Artificial intelligence research is multidisciplinary, relying on both Social media and Space.
His studies deal with areas such as Text mining and Language model as well as Information retrieval.
His work in the fields of Machine learning, such as Learning to rank, overlaps with other areas such as Joint influence, Joint and Work.
His Search engine research is multidisciplinary, incorporating perspectives in Data mining and Information needs.
He has researched Ranking in several fields, including Ranking and Rank.
Between 2016 and 2021, his most popular works were:
- On Application of Learning to Rank for E-Commerce Search (76 citations)
- Non-Autoregressive Machine Translation with Auxiliary Regularization (73 citations)
- Modeling Diverse Relevance Patterns in Ad-hoc Retrieval (41 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Information retrieval, Data mining and Social media.
His work deals with themes such as Synthetic biology and Natural language processing, which intersect with Artificial intelligence.
His biological study spans a wide range of topics, including Text normalization, Variety, Meaning and Generative grammar, Generative model.
ChengXiang Zhai has included themes like Educational technology, Representation and Hidden Markov model in his Machine learning study.
His research in Information retrieval intersects with topics in E-commerce and Categorization.
His work deals with themes such as Matching, Feature, Layer and Set, which intersect with Data mining.
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