What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
His primary areas of investigation include Artificial intelligence, Natural language processing, Artificial neural network, Machine learning and Recurrent neural network.
His Artificial intelligence study frequently draws connections to other fields, such as Context.
His work deals with themes such as Named-entity recognition and Transformer, which intersect with Natural language processing.
His Artificial neural network study also includes fields such as
- Benchmark that connect with fields like Variety and Long short term memory,
- Feature, which have a strong connection to Noise.
His Machine learning research includes elements of Multi-task learning and Automatic summarization.
His Multi-task learning study combines topics from a wide range of disciplines, such as Variety and Training set.
His most cited work include:
- Recurrent Neural Network for Text Classification with Multi-Task Learning (288 citations)
- Adversarial Multi-task Learning for Text Classification (247 citations)
- Long Short-Term Memory Neural Networks for Chinese Word Segmentation (184 citations)
What are the main themes of his work throughout his whole career to date?
Xuanjing Huang mostly deals with Artificial intelligence, Natural language processing, Information retrieval, Machine learning and Artificial neural network.
The various areas that Xuanjing Huang examines in his Artificial intelligence study include Named-entity recognition and Pattern recognition.
He has included themes like Recurrent neural network and Representation in his Natural language processing study.
Xuanjing Huang has researched Information retrieval in several fields, including Social media and Microblogging.
His research integrates issues of Adversarial system, Variety, Data mining and Benchmark in his study of Machine learning.
The concepts of his Segmentation study are interwoven with issues in Feature engineering and Joint.
He most often published in these fields:
- Artificial intelligence (68.99%)
- Natural language processing (43.99%)
- Information retrieval (22.15%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (68.99%)
- Natural language processing (43.99%)
- Machine learning (20.25%)
In recent papers he was focusing on the following fields of study:
Xuanjing Huang mainly focuses on Artificial intelligence, Natural language processing, Machine learning, Automatic summarization and Named-entity recognition.
Xuanjing Huang performs multidisciplinary study in Artificial intelligence and Generalization in his work.
Xuanjing Huang combines subjects such as Embedding and Taxonomy with his study of Natural language processing.
His studies deal with areas such as Adversarial system, Chinese word, Task oriented, Structure and Benchmark as well as Machine learning.
His study on Named-entity recognition also encompasses disciplines like
- Lexicon that intertwine with fields like Character,
- Deep learning together with Variety, k-means clustering, Labeled data, Contextual image classification and Natural language inference.
The study incorporates disciplines such as Margin, Segmentation, Theoretical computer science and Selection in addition to Sentence.
Between 2019 and 2021, his most popular works were:
- K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (76 citations)
- Pre-trained Models for Natural Language Processing: A Survey (59 citations)
- Extractive Summarization as Text Matching (46 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
Xuanjing Huang mainly investigates Artificial intelligence, Natural language processing, Artificial neural network, Information retrieval and Machine learning.
Many of his studies on Artificial intelligence apply to Named-entity recognition as well.
His Natural language processing study integrates concerns from other disciplines, such as Word, Categorization and Taxonomy.
His Word study combines topics in areas such as Margin, Embedding and Training set.
When carried out as part of a general Information retrieval research project, his work on Document summarization and Automatic summarization is frequently linked to work in Qualitative analysis, Graph neural networks and Historical record, therefore connecting diverse disciplines of study.
Within one scientific family, Xuanjing Huang focuses on topics pertaining to Structure under Machine learning, and may sometimes address concerns connected to Base.
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