His primary scientific interests are in Artificial intelligence, Natural language processing, Information retrieval, Sentence and Sentiment analysis. His Artificial intelligence research incorporates elements of Context and Machine learning. His Natural language processing study combines topics in areas such as Speech recognition and Word, SemEval.
The study incorporates disciplines such as Quality and Set in addition to Information retrieval. His research in Sentence intersects with topics in Representation, Negation, Automatic summarization and Ranking SVM. His research investigates the connection with Sentiment analysis and areas like World Wide Web which intersect with concerns in Focus and Scale.
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Information retrieval, Machine translation and Sentence. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Speech recognition. His work deals with themes such as Context and Ranking, which intersect with Natural language processing.
His Information retrieval research is multidisciplinary, incorporating perspectives in Key and Set. Ming Zhou has included themes like NIST, Syntax and Rule-based machine translation in his Machine translation study. His Sentence study combines topics from a wide range of disciplines, such as Representation and Automatic summarization.
Ming Zhou mainly investigates Artificial intelligence, Natural language processing, Electric power system, Transformer and Sentence. His study ties his expertise on Machine learning together with the subject of Artificial intelligence. He has researched Natural language processing in several fields, including Source text and Task.
His research integrates issues of Distributed generation, Renewable energy, Reliability engineering and Flexibility in his study of Electric power system. His Transformer study also includes
Ming Zhou mainly focuses on Artificial intelligence, Natural language processing, Language model, Speech recognition and Transformer. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Code. His research brings together the fields of Transfer of learning and Natural language processing.
His Language model research is multidisciplinary, incorporating elements of Quality, Context, Scale and Product. Ming Zhou has researched Transformer in several fields, including Recurrent neural network, Training set, Overfitting, Word error rate and End-to-end principle. His biological study spans a wide range of topics, including Translation and Source text.
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.
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
Duyu Tang;Furu Wei;Nan Yang;Ming Zhou.
meeting of the association for computational linguistics (2014)
Target-dependent Twitter Sentiment Classification
Long Jiang;Mo Yu;Ming Zhou;Xiaohua Liu.
meeting of the association for computational linguistics (2011)
Recognizing Named Entities in Tweets
Xiaohua Liu;Shaodian Zhang;Furu Wei;Ming Zhou.
meeting of the association for computational linguistics (2011)
Gated Self-Matching Networks for Reading Comprehension and Question Answering
Wenhui Wang;Nan Yang;Furu Wei;Baobao Chang.
meeting of the association for computational linguistics (2017)
Low-Quality Product Review Detection in Opinion Summarization
Jingjing Liu;Yunbo Cao;Chin-Yew Lin;Yalou Huang.
empirical methods in natural language processing (2007)
Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification
Li Dong;Furu Wei;Chuanqi Tan;Duyu Tang.
meeting of the association for computational linguistics (2014)
Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach
Xiaolong Wang;Furu Wei;Xiaohua Liu;Ming Zhou.
conference on information and knowledge management (2011)
Achieving Human Parity on Automatic Chinese to English News Translation
Hany Hassan;Anthony Aue;Chang Chen;Vishal Chowdhary.
arXiv: Computation and Language (2018)
User-level sentiment analysis incorporating social networks
Chenhao Tan;Lillian Lee;Jie Tang;Long Jiang.
knowledge discovery and data mining (2011)
An Empirical Study on Learning to Rank of Tweets
Yajuan Duan;Long Jiang;Tao Qin;Ming Zhou.
international conference on computational linguistics (2010)
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