What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
Xipeng Qiu mainly focuses on Artificial intelligence, Natural language processing, Artificial neural network, Benchmark and Context.
Xipeng Qiu undertakes interdisciplinary study in the fields of Artificial intelligence and Architecture through his works.
The concepts of his Natural language processing study are interwoven with issues in Speech recognition and Transformer.
His work carried out in the field of Artificial neural network brings together such families of science as Feature engineering and Feature.
His Context study deals with Word intersecting with Translation.
His work is dedicated to discovering how Machine learning, Multi-task learning are connected with Training set and Variety and other disciplines.
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?
His main research concerns Artificial intelligence, Natural language processing, Machine learning, Artificial neural network and Segmentation.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Pattern recognition.
He focuses mostly in the field of Pattern recognition, narrowing it down to topics relating to Pooling and, in certain cases, Task.
His studies in Natural language processing integrate themes in fields like Tree, Representation and Transformer.
His Machine learning study integrates concerns from other disciplines, such as Multi-task learning, Training set, Adversarial system and Sequence labeling.
The Artificial neural network study combines topics in areas such as Feature engineering and Benchmark.
He most often published in these fields:
- Artificial intelligence (78.39%)
- Natural language processing (46.23%)
- Machine learning (19.60%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (78.39%)
- Natural language processing (46.23%)
- Automatic summarization (8.54%)
In recent papers he was focusing on the following fields of study:
Xipeng Qiu spends much of his time researching Artificial intelligence, Natural language processing, Automatic summarization, Machine learning and Transformer.
Artificial intelligence is represented through his Language model, Embedding, Deep learning, Word and Task research.
His study focuses on the intersection of Word and fields such as Sentence with connections in the field of Theoretical computer science.
His research in Natural language processing intersects with topics in Representation and Substitution.
His Automatic summarization course of study focuses on Artificial neural network and Task analysis, Visualization and Categorization.
The concepts of his Machine learning study are interwoven with issues in Correctness, Structure, Training set and Knowledge graph.
Between 2019 and 2021, his most popular works were:
- Pre-trained Models for Natural Language Processing: A Survey (59 citations)
- Extractive Summarization as Text Matching (46 citations)
- Heterogeneous Graph Neural Networks for Extractive Document Summarization. (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Natural language processing
Artificial intelligence, Natural language processing, Automatic summarization, Categorization and Machine learning are his primary areas of study.
His study in Task, Machine translation, Joint, Dependency grammar and Text segmentation is carried out as part of his studies in Artificial intelligence.
His work carried out in the field of Natural language processing brings together such families of science as Representation, Translation and Substitution.
His study brings together the fields of Artificial neural network and Automatic summarization.
His study looks at the relationship between Categorization and fields such as Taxonomy, as well as how they intersect with chemical problems.
His Machine learning research also works with subjects such as
- Structure together with Base,
- Language model which connect with Word and Theoretical computer science.
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