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 89 Citations 36,654 374 World Ranking 283 National Ranking 172

Research.com Recognitions

Awards & Achievements

2017 - ACM Fellow For contributions to information retrieval and text data mining

2009 - ACM Distinguished Member

2008 - Fellow of Alfred P. Sloan Foundation

Overview

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.

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

A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval

Chengxiang Zhai;John Lafferty.
international acm sigir conference on research and development in information retrieval (2001)

1779 Citations

A study of smoothing methods for language models applied to information retrieval

Chengxiang Zhai;John Lafferty.
ACM Transactions on Information Systems (2004)

1449 Citations

Mining Text Data

Charu C. Aggarwal;Cheng Xiang Zhai.
(2012)

1400 Citations

Document Language Models, Query Models, and Risk Minimization for Information Retrieval

John Lafferty;Chengxiang Zhai.
international acm sigir conference on research and development in information retrieval (2001)

1040 Citations

Topic sentiment mixture: modeling facets and opinions in weblogs

Qiaozhu Mei;Xu Ling;Matthew Wondra;Hang Su.
the web conference (2007)

937 Citations

Big data: Astronomical or genomical?

Zachary D. Stephens;Skylar Y. Lee;Faraz Faghri;Roy H. Campbell.
PLOS Biology (2015)

936 Citations

Model-based feedback in the language modeling approach to information retrieval

Chengxiang Zhai;John Lafferty.
conference on information and knowledge management (2001)

871 Citations

Instance Weighting for Domain Adaptation in NLP

Jing Jiang;ChengXiang Zhai.
meeting of the association for computational linguistics (2007)

796 Citations

A survey of text classification algorithms

Charu C. Aggarwal;Cheng Xiang Zhai.
Mining Text Data (2012)

770 Citations

Discovering evolutionary theme patterns from text: an exploration of temporal text mining

Qiaozhu Mei;ChengXiang Zhai.
knowledge discovery and data mining (2005)

703 Citations

Best Scientists Citing ChengXiang Zhai

W. Bruce Croft

W. Bruce Croft

University of Massachusetts Amherst

Publications: 120

Maarten de Rijke

Maarten de Rijke

University of Amsterdam

Publications: 116

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 114

Jian-Yun Nie

Jian-Yun Nie

University of Montreal

Publications: 74

Dawei Song

Dawei Song

Beijing Institute of Technology

Publications: 65

Gerhard Weikum

Gerhard Weikum

Max Planck Institute for Informatics

Publications: 58

Bing Liu

Bing Liu

Peking University

Publications: 57

James Allan

James Allan

University of Massachusetts Amherst

Publications: 53

Hang Li

Hang Li

ByteDance

Publications: 52

Iadh Ounis

Iadh Ounis

University of Glasgow

Publications: 51

Xueqi Cheng

Xueqi Cheng

Chinese Academy of Sciences

Publications: 51

Jamie Callan

Jamie Callan

Carnegie Mellon University

Publications: 50

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 49

Jie Tang

Jie Tang

Tsinghua University

Publications: 46

Krisztian Balog

Krisztian Balog

University of Stavanger

Publications: 45

Craig Macdonald

Craig Macdonald

University of Glasgow

Publications: 44

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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