H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 45 Citations 8,610 238 World Ranking 3592 National Ranking 334

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Natural language processing

His main research concerns Artificial intelligence, Natural language processing, Machine learning, Tree kernel and Feature. The Artificial intelligence study combines topics in areas such as Data mining and Pattern recognition. Guodong Zhou focuses mostly in the field of Natural language processing, narrowing it down to matters related to Speech recognition and, in some cases, Orthographic projection.

His Machine learning research incorporates themes from Class and Coreference. His Tree kernel study combines topics from a wide range of disciplines, such as Relation, Parse tree, Information retrieval and Kernel. The study incorporates disciplines such as Information extraction, Inference, Knowledge extraction and Identification in addition to Feature.

His most cited work include:

  • Exploring Various Knowledge in Relation Extraction (553 citations)
  • Named Entity Recognition using an HMM-based Chunk Tagger (538 citations)
  • A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features (214 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Artificial intelligence, Natural language processing, Machine learning, Parsing and Pattern recognition. His research related to Sentence, Parse tree, Tree kernel, Feature and Machine translation might be considered part of Artificial intelligence. The various areas that he examines in his Parse tree study include Tree traversal, Tree structure and Predicate.

His studies in Tree kernel integrate themes in fields like Tree and Resolution. His Natural language processing research is multidisciplinary, relying on both Context, Speech recognition, Word and Focus. In general Machine learning, his work in Semi-supervised learning, Discriminative model and Artificial neural network is often linked to Empirical research linking many areas of study.

He most often published in these fields:

  • Artificial intelligence (83.50%)
  • Natural language processing (60.84%)
  • Machine learning (18.45%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (83.50%)
  • Natural language processing (60.84%)
  • Artificial neural network (6.47%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Artificial intelligence, Natural language processing, Artificial neural network, Sentence and Focus. Guodong Zhou interconnects Machine learning and Modal in the investigation of issues within Artificial intelligence. His Natural language processing study integrates concerns from other disciplines, such as Context, Word, Representation, Modality and Benchmark.

The concepts of his Context study are interwoven with issues in Dependency and Negation. His work carried out in the field of Artificial neural network brings together such families of science as Document level, Leverage and Stance detection. His Focus research is multidisciplinary, incorporating elements of Discourse relation, Feature and Treebank.

Between 2018 and 2021, his most popular works were:

  • Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (32 citations)
  • Modeling both Context- and Speaker-Sensitive Dependence for Emotion Detection in Multi-speaker Conversations (29 citations)
  • Ag-Doped Halide Perovskite Nanocrystals for Tunable Band Structure and Efficient Charge Transport (29 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Natural language processing

Guodong Zhou mainly investigates Artificial intelligence, Natural language processing, Word, Optoelectronics and Transformer. His Artificial intelligence study deals with Machine learning intersecting with Conversation. His Natural language processing research focuses on Sentiment analysis in particular.

His Word research includes themes of Context and Machine translation. His biological study spans a wide range of topics, including Emotion detection and Speech recognition. His Transformer research incorporates elements of Translation, Theoretical computer science, Text generation and Graph.

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

Named Entity Recognition using an HMM-based Chunk Tagger

GuoDong Zhou;Jian Su.
meeting of the association for computational linguistics (2002)

889 Citations

Exploring Various Knowledge in Relation Extraction

GuoDong Zhou;Jian Su;Jie Zhang;Min Zhang.
meeting of the association for computational linguistics (2005)

778 Citations

A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features

Min Zhang;Jie Zhang;Jian Su;GuoDong Zhou.
meeting of the association for computational linguistics (2006)

349 Citations

Recognizing names in biomedical texts: a machine learning approach

Guodong Zhou;Jie Zhang;Jian Su;Dan Shen.
Bioinformatics (2004)

313 Citations

Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information

GuoDong Zhou;Min Zhang;DongHong Ji;QiaoMing Zhu.
empirical methods in natural language processing (2007)

281 Citations

Multi-Criteria-based Active Learning for Named Entity Recognition

Dan Shen;Jie Zhang;Jian Su;Guodong Zhou.
meeting of the association for computational linguistics (2004)

235 Citations

Using Cross-Entity Inference to Improve Event Extraction

Yu Hong;Jianfeng Zhang;Bin Ma;Jianmin Yao.
meeting of the association for computational linguistics (2011)

231 Citations

Coreference Resolution Using Competition Learning Approach

Xiaofeng Yang;Guodong Zhou;Jian Su;Chew Lim Tan.
meeting of the association for computational linguistics (2003)

231 Citations

Semi-supervised learning for imbalanced sentiment classification

Shoushan Li;Zhongqing Wang;Guodong Zhou;Sophia Yat Mei Lee.
international joint conference on artificial intelligence (2011)

209 Citations

Effective Adaptation of Hidden Markov Model-based Named Entity Recognizer for Biomedical Domain

Dan Shen;Jie Zhang;Guodong Zhou;Jian Su.
meeting of the association for computational linguistics (2003)

154 Citations

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