What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary areas of investigation include Artificial intelligence, Information retrieval, Machine learning, Natural language processing and Pattern recognition.
Yangqiu Song regularly links together related areas like Collaborative filtering in his Artificial intelligence studies.
His work deals with themes such as Text mining and Interactive visualization, which intersect with Information retrieval.
His research in the fields of Transfer of learning and Decision boundary overlaps with other disciplines such as Contextual image classification.
His Natural language processing study combines topics in areas such as Deep learning, Convolutional neural network, Leverage and Domain knowledge.
His work investigates the relationship between Pattern recognition and topics such as Cluster analysis that intersect with problems in Data set and Parallel algorithm.
His most cited work include:
- Parallel Spectral Clustering in Distributed Systems (449 citations)
- TextFlow: Towards Better Understanding of Evolving Topics in Text (266 citations)
- Short text conceptualization using a probabilistic knowledgebase (182 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Natural language processing, Machine learning, Information retrieval and Data mining.
His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition.
His Natural language processing research incorporates themes from Context, Relation, Pronoun and Coreference.
His Machine learning research is multidisciplinary, relying on both Crowdsourcing and Usability.
His Information retrieval study integrates concerns from other disciplines, such as Text mining, Social media and Knowledge base.
Data mining is frequently linked to Cluster analysis in his study.
He most often published in these fields:
- Artificial intelligence (55.74%)
- Natural language processing (23.83%)
- Machine learning (20.00%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (55.74%)
- Natural language processing (23.83%)
- Commonsense knowledge (7.23%)
In recent papers he was focusing on the following fields of study:
Yangqiu Song focuses on Artificial intelligence, Natural language processing, Commonsense knowledge, Theoretical computer science and Benchmark.
His Artificial intelligence study incorporates themes from Crowdsourcing, Machine learning and Pattern recognition.
His research in Natural language processing intersects with topics in Context, Relation and Resolution.
Yangqiu Song has included themes like Language model, Representation and Data science in his Commonsense knowledge study.
His biological study spans a wide range of topics, including Logical consequence, Task analysis and Directed graph.
His research on Benchmark also deals with topics like
- Margin most often made with reference to Algorithm,
- Node and related Link,
- Ideal, which have a strong connection to Interpretability.
Between 2019 and 2021, his most popular works were:
- TransOMCS: From Linguistic Graphs to Commonsense Knowledge (14 citations)
- Event detection and evolution in multi-lingual social streams (13 citations)
- ASER: A Large-scale Eventuality Knowledge Graph (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary areas of study are Artificial intelligence, Natural language processing, Theoretical computer science, Commonsense knowledge and Data science.
In most of his Artificial intelligence studies, his work intersects topics such as Pattern recognition.
The study incorporates disciplines such as Discourse relation and Representation in addition to Natural language processing.
His work in the fields of Theoretical computer science, such as PageRank, overlaps with other areas such as Motif.
He interconnects Categorization, Commonsense reasoning and Leverage in the investigation of issues within Commonsense knowledge.
His study in Data science is interdisciplinary in nature, drawing from both Relation, Inference, Focus and Knowledge graph.
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