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, Machine learning, Information retrieval, Automatic image annotation and Natural language processing.
His works in Artificial neural network and Relationship extraction are all subjects of inquiry into Artificial intelligence.
His biological study spans a wide range of topics, including Natural language generation, Parsing and Relation.
His Machine learning research is multidisciplinary, incorporating perspectives in Attention network and SemEval.
Yansong Feng works mostly in the field of Information retrieval, limiting it down to concerns involving Image retrieval and, occasionally, Topic model.
His Natural language processing research includes a combination of various areas of study, such as Context and Meaning.
His most cited work include:
- Question Answering on Freebase via Relation Extraction and Textual Evidence (174 citations)
- Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling (158 citations)
- Topic Models for Image Annotation and Text Illustration (104 citations)
What are the main themes of his work throughout his whole career to date?
Yansong Feng spends much of his time researching Artificial intelligence, Natural language processing, Machine learning, Artificial neural network and Information retrieval.
His study involves Parsing, Knowledge base, Inference, Relationship extraction and Question answering, a branch of Artificial intelligence.
Yansong Feng interconnects Word and Benchmark in the investigation of issues within Natural language processing.
In his research, Range is intimately related to Training set, which falls under the overarching field of Machine learning.
His work carried out in the field of Artificial neural network brings together such families of science as Theoretical computer science and Graph.
His Information retrieval research focuses on subjects like Automatic image annotation, which are linked to Automatic summarization.
He most often published in these fields:
- Artificial intelligence (55.08%)
- Natural language processing (27.12%)
- Machine learning (22.03%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (55.08%)
- Machine learning (22.03%)
- Data mining (11.86%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Data mining, Construct and Constraint.
His study ties his expertise on Pattern recognition together with the subject of Artificial intelligence.
His research investigates the connection between Machine learning and topics such as Parsing that intersect with problems in Relevance.
His Data mining research incorporates themes from Matching, Similarity and Discriminative model.
As part of one scientific family, Yansong Feng deals mainly with the area of Theoretical computer science, narrowing it down to issues related to the Language model, and often Word.
His Benchmark research incorporates elements of Paragraph and Natural language processing.
Between 2019 and 2021, his most popular works were:
- Neighborhood Matching Network for Entity Alignment (8 citations)
- Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment (7 citations)
- Combining Graph-Based Learning With Automated Data Collection for Code Vulnerability Detection (3 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
Yansong Feng mainly investigates Data mining, Discriminative model, Structure, Similarity and Construct.
The concepts of his Data mining study are interwoven with issues in Cross lingual and Knowledge graph.
His Discriminative model study frequently links to related topics such as Matching.
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.
