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 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.
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.
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.
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Named Entity Recognition using an HMM-based Chunk Tagger
GuoDong Zhou;Jian Su.
meeting of the association for computational linguistics (2002)
Exploring Various Knowledge in Relation Extraction
GuoDong Zhou;Jian Su;Jie Zhang;Min Zhang.
meeting of the association for computational linguistics (2005)
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)
Recognizing names in biomedical texts: a machine learning approach
Guodong Zhou;Jie Zhang;Jian Su;Dan Shen.
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)
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)
Using Cross-Entity Inference to Improve Event Extraction
Yu Hong;Jianfeng Zhang;Bin Ma;Jianmin Yao.
meeting of the association for computational linguistics (2011)
Coreference Resolution Using Competition Learning Approach
Xiaofeng Yang;Guodong Zhou;Jian Su;Chew Lim Tan.
meeting of the association for computational linguistics (2003)
Semi-supervised learning for imbalanced sentiment classification
Shoushan Li;Zhongqing Wang;Guodong Zhou;Sophia Yat Mei Lee.
international joint conference on artificial intelligence (2011)
Hidden Structure Ordering Along Backbone of Fused-Ring Electron Acceptors Enhanced by Ternary Bulk Heterojunction
Jiangquan Mai;Yiqun Xiao;Guodong Zhou;Jiayu Wang.
Advanced Materials (2018)
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