What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
Artificial intelligence, Natural language processing, Information retrieval, Machine learning and Text corpus are his primary areas of study.
His work often combines Artificial intelligence and Context studies.
He works in the field of Natural language processing, namely Text mining.
The study incorporates disciplines such as Bayesian probability and Social network in addition to Information retrieval.
His Machine learning research incorporates elements of Named-entity recognition and Sequence labeling.
Knowledge-based systems, Taxonomy, Topic model and Object is closely connected to Information extraction in his research, which is encompassed under the umbrella topic of Text corpus.
His most cited work include:
- Hierarchical graph representation learning with differentiable pooling (558 citations)
- Personalized entity recommendation: a heterogeneous information network approach (362 citations)
- GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models (172 citations)
What are the main themes of his work throughout his whole career to date?
Xiang Ren focuses on Artificial intelligence, Natural language processing, Machine learning, Text corpus and Relationship extraction.
In his works, Xiang Ren performs multidisciplinary study on Artificial intelligence and Context.
His work on Sentence, Question answering, Phrase and Parsing as part of general Natural language processing research is frequently linked to Synonym, thereby connecting diverse disciplines of science.
Xiang Ren combines subjects such as Embedding and Sequence labeling with his study of Machine learning.
His Text corpus research includes elements of Knowledge base, Text mining, Knowledge extraction, Social media and String.
His study in Relationship extraction is interdisciplinary in nature, drawing from both Sentiment analysis, Labeled data, Training set and Leverage.
He most often published in these fields:
- Artificial intelligence (66.06%)
- Natural language processing (34.84%)
- Machine learning (28.05%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (66.06%)
- Natural language processing (34.84%)
- Language model (12.22%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Natural language processing, Language model, Machine learning and Question answering.
Reinforcement learning, Transformer, Benchmark, Transfer of learning and Automatic summarization are the core of his Artificial intelligence study.
His Natural language processing research focuses on Commonsense reasoning and how it connects with Generative grammar.
His research integrates issues of Natural language understanding, Commonsense knowledge and Set in his study of Language model.
His Machine learning research focuses on subjects like Inference, which are linked to Robustness.
Xiang Ren focuses mostly in the field of Question answering, narrowing it down to matters related to Knowledge graph and, in some cases, Graph, Theoretical computer science and Interpretability.
Between 2019 and 2021, his most popular works were:
- Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models (23 citations)
- CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning (17 citations)
- NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
Xiang Ren mostly deals with Artificial intelligence, Natural language processing, Language model, Machine learning and Sentence.
His study in Artificial intelligence concentrates on Benchmark, Natural language, Sequence labeling, Annotation and False positive paradox.
His research in Natural language processing intersects with topics in Semantics, Construct and Named-entity recognition.
His Language model study also includes fields such as
- Commonsense knowledge which intersects with area such as Information retrieval,
- Set which intersects with area such as Generative grammar and Commonsense reasoning.
In Machine learning, Xiang Ren works on issues like Inference, which are connected to Robustness.
As a member of one scientific family, Xiang Ren mostly works in the field of Relationship extraction, focusing on Labeled data and, on occasion, Text corpus, Training set and Artificial neural network.
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