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, Word, Sentiment analysis and Representation.
Artificial neural network, SemEval, Sentence, Feature and Semantic similarity are the subjects of his Artificial intelligence studies.
Document level, Recurrent neural network and Time delay neural network is closely connected to Semantics in his research, which is encompassed under the umbrella topic of Artificial neural network.
The Feature study combines topics in areas such as Support vector machine, Pattern recognition and Lexicon.
His studies in Natural language processing integrate themes in fields like Supervised learning, Chinese characters, Deep learning and Word embedding.
He has researched Sentiment analysis in several fields, including Feature engineering and Embedding.
His most cited work include:
- Document Modeling with Gated Recurrent Neural Network for Sentiment Classification (829 citations)
- Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification (637 citations)
- SemEval-2016 task 5 : aspect based sentiment analysis (403 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, Sentence, Sentiment analysis and Word.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition.
His Natural language processing study integrates concerns from other disciplines, such as Annotation, Semantics and SemEval.
His Sentence research integrates issues from Classifier, Speech recognition and Convolutional neural network.
Bing Qin interconnects Recurrent neural network, Embedding, Word embedding, Feature engineering and Phrase in the investigation of issues within Sentiment analysis.
As part of the same scientific family, Bing Qin usually focuses on Word, concentrating on Feature and intersecting with Feature learning.
He most often published in these fields:
- Artificial intelligence (70.00%)
- Natural language processing (50.67%)
- Sentence (20.67%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (70.00%)
- Natural language processing (50.67%)
- Sentence (20.67%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Sentence, Automatic summarization and Utterance.
Deep learning, Question answering, Artificial neural network, Sentiment analysis and Parsing are the core of his Artificial intelligence study.
His research in Artificial neural network intersects with topics in Dependency and Discourse structure.
His studies deal with areas such as Recurrent neural network and Web information as well as Natural language processing.
His Sentence research integrates issues from Object and Subject.
His Language model study combines topics from a wide range of disciplines, such as SemEval and Natural language.
Between 2019 and 2021, his most popular works were:
- How Does Selective Mechanism Improve Self-Attention Networks? (9 citations)
- A syntactic path-based hybrid neural network for negation scope detection (9 citations)
- Joint Learning of Question Answering and Question Generation (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
Bing Qin mostly deals with Artificial intelligence, Natural language processing, Question answering, Sentence and Automatic summarization.
His work on Discourse structure, Artificial neural network and Dependency as part of general Artificial intelligence study is frequently linked to Machine reading and Graph neural networks, therefore connecting diverse disciplines of science.
His Natural language processing research is multidisciplinary, incorporating elements of Deep learning and SemEval.
His Question answering research is multidisciplinary, incorporating perspectives in Probabilistic logic, Machine learning, Task analysis and Knowledge extraction.
The Sentence study combines topics in areas such as Self attention, Word order and Machine translation.
Bing Qin has included themes like Commonsense knowledge and Utterance in his Automatic summarization study.
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