What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Artificial intelligence, Natural language processing, Machine learning, RDF and Graph.
Artificial neural network, Parsing, Relationship extraction, Representation and Semantics are the subjects of his Artificial intelligence studies.
His work on Question answering as part of general Natural language processing research is frequently linked to Metric, thereby connecting diverse disciplines of science.
His Machine learning study integrates concerns from other disciplines, such as Attention network and SemEval.
His work in the fields of SPARQL, RDF/XML and RDF Schema overlaps with other areas such as RDF query language.
The concepts of his Graph study are interwoven with issues in Lattice graph, Graph edit distance, Graph and Distance-hereditary graph.
His most cited work include:
- Style Transfer in Text: Exploration and Evaluation. (229 citations)
- gStore: answering SPARQL queries via subgraph matching (202 citations)
- Natural language question answering over RDF: a graph data driven approach (180 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Natural language processing, Information retrieval, Theoretical computer science and Machine learning.
Dongyan Zhao merges many fields, such as Artificial intelligence and Conversation, in his writings.
His Natural language processing research incorporates elements of Autoencoder, Generative grammar, Dialog box and Benchmark.
His Theoretical computer science research integrates issues from Graph, Distance-hereditary graph, Knowledge graph and Subgraph isomorphism problem, Graph.
Dongyan Zhao has researched Machine learning in several fields, including Relationship extraction, Training set and Inference.
The Question answering study combines topics in areas such as Relation and Natural language.
He most often published in these fields:
- Artificial intelligence (43.78%)
- Natural language processing (27.04%)
- Information retrieval (23.18%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (43.78%)
- Information retrieval (23.18%)
- Selection (8.58%)
In recent papers he was focusing on the following fields of study:
Dongyan Zhao focuses on Artificial intelligence, Information retrieval, Selection, Natural language processing and Human–computer interaction.
His work carried out in the field of Artificial intelligence brings together such families of science as Consistency and Machine learning.
In his study, which falls under the umbrella issue of Information retrieval, Generative grammar and Question answering is strongly linked to E-commerce.
Dongyan Zhao has included themes like Deep learning and Translation in his Natural language processing study.
His Human–computer interaction study combines topics from a wide range of disciplines, such as Field, Utterance and Dialog box.
His work deals with themes such as Graph and Relation, which intersect with Theoretical computer science.
Between 2019 and 2021, his most popular works were:
- Low-Resource Knowledge-Grounded Dialogue Generation (20 citations)
- Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog (11 citations)
- A Character-Centric Neural Model for Automated Story Generation (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Dongyan Zhao spends much of his time researching Information retrieval, Selection, Human–computer interaction, Artificial intelligence and Structure.
His research integrates issues of Frame and Dialog box in his study of Information retrieval.
His studies in Artificial intelligence integrate themes in fields like Consistency and Machine learning.
Many of his research projects under Machine learning are closely connected to Scheme and Hull with Scheme and Hull, tying the diverse disciplines of science together.
His Automatic summarization study incorporates themes from Web page, Focus and Search engine.
His work on Natural language processing expands to the thematically related Autoencoder.
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.
