What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
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.
His most cited work include:
- Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification (637 citations)
- Target-dependent Twitter Sentiment Classification (592 citations)
- Gated Self-Matching Networks for Reading Comprehension and Question Answering (463 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (51.05%)
- Natural language processing (39.87%)
- Information retrieval (18.14%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (51.05%)
- Natural language processing (39.87%)
- Electric power system (10.13%)
In recent papers he was focusing on the following fields of study:
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
- Encoder which connect with Speech recognition,
- Overfitting and related Training set.
His Machine translation research integrates issues from Multi-task learning, Translation and Document level.
Between 2019 and 2021, his most popular works were:
- K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (76 citations)
- XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation (64 citations)
- UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training (56 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Statistics
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.
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