What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Dawei Song mainly focuses on Artificial intelligence, Information retrieval, Relevance, Language model and Natural language processing.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Quantum cognition and Pattern recognition.
The Information retrieval study combines topics in areas such as Image, Data mining and Feature vector.
His studies in Relevance integrate themes in fields like Semantic computing, Context, Information needs and Semantic search.
His study in the fields of Sentence under the domain of Natural language processing overlaps with other disciplines such as Information system, Track and Open university.
His research in Sentence focuses on subjects like Set, which are connected to Query language and Query expansion.
His most cited work include:
- Query expansion using term relationships in language models for information retrieval (167 citations)
- Toward a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning (137 citations)
- Exploring EEG Features in Cross-Subject Emotion Recognition (84 citations)
What are the main themes of his work throughout his whole career to date?
Dawei Song mainly investigates Information retrieval, Artificial intelligence, Natural language processing, Relevance and Query expansion.
His Information retrieval research is multidisciplinary, incorporating elements of Data mining and Personalization.
His studies deal with areas such as Context, Machine learning and Pattern recognition as well as Artificial intelligence.
His Natural language processing study combines topics from a wide range of disciplines, such as Dependency and Inference.
His Relevance research also works with subjects such as
- Cognition that connect with fields like Quantum cognition,
- Theoretical computer science and related Structure and Information needs.
His study looks at the relationship between Query expansion and topics such as Query language, which overlap with Sargable and RDF query language.
He most often published in these fields:
- Information retrieval (53.73%)
- Artificial intelligence (46.27%)
- Natural language processing (22.36%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (46.27%)
- Natural language processing (22.36%)
- Relevance (20.50%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Natural language processing, Relevance, Information retrieval and Sentiment analysis.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Machine learning.
His Natural language processing study combines topics in areas such as Context, Convolution, Representation, Hilbert space and SemEval.
His research in Relevance intersects with topics in Learning to rank, Theoretical computer science, Cognition, Construct and Session.
His Information retrieval study integrates concerns from other disciplines, such as Feature and Dimension.
His Sentiment analysis study also includes
- Conversation together with Interaction information,
- Key which intersects with area such as Semantic gap.
Between 2016 and 2021, his most popular works were:
- Exploring EEG Features in Cross-Subject Emotion Recognition (84 citations)
- Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks. (61 citations)
- A Tensorized Transformer for Language Modeling (42 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Sentiment analysis, Representation and Sentence.
He conducts interdisciplinary study in the fields of Artificial intelligence and Quantum probability through his works.
His study in Sentiment analysis is interdisciplinary in nature, drawing from both Range and Conversation.
His work carried out in the field of Representation brings together such families of science as Language model and Word.
His Word study deals with Theoretical computer science intersecting with Information needs, Session search and Word embedding.
Dawei Song has included themes like Artificial neural network and Quantum in his Sentence study.
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