What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
Maosong Sun mostly deals with Artificial intelligence, Natural language processing, Machine learning, Relationship extraction and Relation.
Maosong Sun combines topics linked to Source code with his work on Artificial intelligence.
His Natural language processing research is multidisciplinary, incorporating perspectives in Speech recognition, Word and Knowledge graph.
His Machine learning study which covers Relation classification that intersects with Robustness.
In Relationship extraction, Maosong Sun works on issues like Inference, which are connected to Latent Dirichlet allocation.
His work deals with themes such as Range, Embedding, Theoretical computer science and Shot, which intersect with Relation.
His most cited work include:
- Learning entity and relation embeddings for knowledge graph completion (1320 citations)
- Graph Neural Networks: A Review of Methods and Applications (701 citations)
- Network representation learning with rich text information (611 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, Machine learning, Word and Information retrieval.
His research links Speech recognition with Artificial intelligence.
The study incorporates disciplines such as Relation, Segmentation and Sememe in addition to Natural language processing.
His Machine learning research integrates issues from Training set and Data mining.
His Word study integrates concerns from other disciplines, such as Similarity and Lexicon.
The various areas that he examines in his Information retrieval study include Context, Social media and Graph.
He most often published in these fields:
- Artificial intelligence (64.57%)
- Natural language processing (40.68%)
- Machine learning (19.16%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (64.57%)
- Natural language processing (40.68%)
- Machine learning (19.16%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Natural language processing, Machine learning, Language model and Source code are his primary areas of study.
His Artificial intelligence study is mostly concerned with Machine translation, Relationship extraction, Natural language, Word and Speech processing.
Maosong Sun combines subjects such as Decoding methods and Translation with his study of Machine translation.
He focuses mostly in the field of Natural language processing, narrowing it down to matters related to Context and, in some cases, Pattern matching, Component and Key.
His Machine learning research incorporates elements of Adversarial system and Robustness.
The Source code study combines topics in areas such as Computer security, Speech recognition and Graph.
Between 2019 and 2021, his most popular works were:
- Word-level Textual Adversarial Attacking as Combinatorial Optimization (43 citations)
- Fine-grained Fact Verification with Kernel Graph Attention Network (33 citations)
- Coreferential Reasoning Learning for Language Representation (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
Maosong Sun mainly focuses on Artificial intelligence, Natural language processing, Source code, Relationship extraction and Machine learning.
His work on Knowledge graph, Deep learning and Inference as part of general Artificial intelligence study is frequently linked to Civil law and Legal profession, bridging the gap between disciplines.
His Natural language processing study combines topics from a wide range of disciplines, such as Sememe and Knowledge base.
His Source code research is multidisciplinary, relying on both Attention network, Graph and Computer security, Attack model.
His research in Relationship extraction tackles topics such as Key which are related to areas like Data science.
His work on Transformer as part of general Machine learning research is frequently linked to Single shot, Social disruption and Sequence, bridging the gap between disciplines.
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