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Shunnan Xu

dblp:241/7224 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 38% Knowledge graphs · 38% Information retrieval · 23%
Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition › knowledge extraction
concept extraction		 0.412019
A User-Centered Concept Mining System for Query and Document Understanding at Tencent · KDD 2019
Data mining › text mining › information extraction
concept mining		 0.412019
A User-Centered Concept Mining System for Query and Document Understanding at Tencent · KDD 2019
Knowledge graphs
taxonomy construction		 0.412019
A User-Centered Concept Mining System for Query and Document Understanding at Tencent · KDD 2019
Information retrieval › query understanding
query analysis		 0.112019
A User-Centered Concept Mining System for Query and Document Understanding at Tencent · KDD 2019
Information retrieval
query understanding		 0.112019
A User-Centered Concept Mining System for Query and Document Understanding at Tencent · KDD 2019

Methods — techniques the papers use, named apart from their topics

concept mining · 0.8
YearPublicationVenuePosition
2019 A User-Centered Concept Mining System for Query and Document Understanding at Tencent
abstract
Concepts embody the knowledge of the world and facilitate the cognitive processes of human beings. Mining concepts from web documents and constructing the corresponding taxonomy are core research problems in text understanding and support many downstream tasks such as query analysis, knowledge base construction, recommendation, and search. However, we argue that most prior studies extract formal and overly general concepts from Wikipedia or static web pages, which are not representing the user perspective.
Bang Liu 0003, Weidong Guo, Di Niu 0002, Shunnan Xu, Jinghong Lin, Kunfeng Lai
 KDD5