What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
Minlie Huang mostly deals with Artificial intelligence, Natural language processing, Machine learning, Information retrieval and Conversation.
In his research, Minlie Huang performs multidisciplinary study on Artificial intelligence and Protein–protein interaction.
His study in Natural language processing is interdisciplinary in nature, drawing from both Annotation and Conjunction.
His work deals with themes such as Information extraction and Forum spam, Spambot, which intersect with Machine learning.
Minlie Huang combines subjects such as Ranking, Context, World Wide Web and Biological database with his study of Information retrieval.
His Conversation research includes themes of Commonsense knowledge and Vocabulary.
His most cited work include:
- Attention-based LSTM for Aspect-level Sentiment Classification (696 citations)
- Learning to identify review spam (253 citations)
- Structure-Aware Review Mining and Summarization (234 citations)
What are the main themes of his work throughout his whole career to date?
Minlie Huang mainly focuses on Artificial intelligence, Natural language processing, Machine learning, Human–computer interaction and Dialog box.
Artificial intelligence is closely attributed to Context in his research.
His Natural language processing research incorporates themes from Commonsense knowledge and Comprehension.
His work carried out in the field of Machine learning brings together such families of science as Text mining, Generative model and Robustness.
The Human–computer interaction study combines topics in areas such as Semantics, Conversation, State and Reinforcement learning.
His Dialog box research is multidisciplinary, relying on both Task oriented and Component.
He most often published in these fields:
- Artificial intelligence (54.17%)
- Natural language processing (32.87%)
- Machine learning (17.59%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (54.17%)
- Dialog box (16.67%)
- Human–computer interaction (17.13%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Dialog box, Human–computer interaction, Natural language processing and Conversation.
Artificial intelligence and Machine learning are frequently intertwined in his study.
His work in the fields of Leverage overlaps with other areas such as Beam search.
His Dialog system study in the realm of Dialog box connects with subjects such as Multi domain.
His studies deal with areas such as Variety, Component and Reinforcement learning as well as Human–computer interaction.
His work deals with themes such as Context, Self training and Knowledge graph, which intersect with Natural language processing.
Between 2019 and 2021, his most popular works were:
- Challenges in Building Intelligent Open-domain Dialog Systems (46 citations)
- A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation (42 citations)
- Robustness Verification for Transformers (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
The scientist’s investigation covers issues in Dialog box, Artificial intelligence, Natural language processing, Human–computer interaction and Dialog system.
He has included themes like Domain, Semantics, Conversation and Component in his Dialog box study.
His study in Repetition extends to Artificial intelligence with its themes.
His study in the field of Question answering also crosses realms of Base.
His research in Human–computer interaction intersects with topics in Natural language, Decomposition, Domain knowledge and Reinforcement learning.
The Dialog system study combines topics in areas such as Interpersonal communication and Task oriented.
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.
