What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
His primary areas of study are Artificial intelligence, Machine learning, Data mining, Natural language processing and Theoretical computer science.
His biological study spans a wide range of topics, including Matching, Relevance and Pattern recognition.
He has included themes like Database transaction and Representation in his Machine learning study.
The various areas that Xueqi Cheng examines in his Data mining study include Embedding, Feature selection, Statistical model and Robustness.
His Natural language processing study which covers Context that intersects with Matching, Viterbi algorithm and Sequence.
His research integrates issues of CURE data clustering algorithm, Locality, Random graph and Clique percolation method, Community structure in his study of Theoretical computer science.
His most cited work include:
- A biterm topic model for short texts (557 citations)
- Detect overlapping and hierarchical community structure in networks (535 citations)
- Significance and Challenges of Big Data Research (377 citations)
What are the main themes of his work throughout his whole career to date?
Xueqi Cheng mainly focuses on Artificial intelligence, Machine learning, Information retrieval, Data mining and Natural language processing.
His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Pattern recognition.
His study in the field of Leverage also crosses realms of Rank and Process.
His Information retrieval study integrates concerns from other disciplines, such as Web page and The Internet.
Xueqi Cheng regularly links together related areas like Word in his Natural language processing studies.
His studies link Ranking with Ranking.
He most often published in these fields:
- Artificial intelligence (42.59%)
- Machine learning (20.56%)
- Information retrieval (20.19%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (42.59%)
- Natural language processing (13.33%)
- Machine learning (20.56%)
In recent papers he was focusing on the following fields of study:
Xueqi Cheng mainly investigates Artificial intelligence, Natural language processing, Machine learning, Theoretical computer science and Information retrieval.
His work on Sentence as part of his general Natural language processing study is frequently connected to Structure, thereby bridging the divide between different branches of science.
Many of his research projects under Machine learning are closely connected to Popularity, Function and Mechanism with Popularity, Function and Mechanism, tying the diverse disciplines of science together.
The concepts of his Theoretical computer science study are interwoven with issues in Embedding, Graph and Relation.
Xueqi Cheng combines subjects such as Ranking and Natural language with his study of Information retrieval.
In his research on the topic of Question answering, Word is strongly related with Matching.
Between 2017 and 2021, his most popular works were:
- Learning to Control the Specificity in Neural Response Generation (60 citations)
- A Deep Look into neural ranking models for information retrieval (51 citations)
- Graph Wavelet Neural Network. (49 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Xueqi Cheng spends much of his time researching Artificial intelligence, Information retrieval, Embedding, Natural language processing and Machine learning.
His Artificial intelligence study frequently links to adjacent areas such as Graph.
His work on Relevance as part of general Information retrieval study is frequently linked to Distribution, therefore connecting diverse disciplines of science.
His Embedding research includes themes of Data modeling, Theoretical computer science, Reliability, Space and Translation.
His research in the fields of Sentence overlaps with other disciplines such as Structure.
He interconnects Node, Representation, Conversation and Metric in the investigation of issues within Machine learning.
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