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Ming Zhong 0013

dblp:92/2292-13 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2006
—ORCID · unresolved

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

Databases, data management, data science and information retrieval · 1 · 1 first-author

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
Information retrieval · 88% Data mining · 12%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › document retrieval › domain-specific retrieval
biomedical information retrieval		 0.112006
Concept-based biomedical text retrieval · SIGIR 2006
Information retrieval › document retrieval
concept-based retrieval		 0.112006
Concept-based biomedical text retrieval · SIGIR 2006
Data mining › text mining › information extraction
concept mining		 0.012006
Concept-based biomedical text retrieval · SIGIR 2006
Information retrieval
query processing		 0.012006
Concept-based biomedical text retrieval · SIGIR 2006
YearPublicationVenuePosition
2006 Concept-based biomedical text retrieval
abstract
One challenging problem for biomedical text retrieval is to find accurate synonyms or name variants for biomedical entities. In this paper, we propose a new concept-based approach to tackle this problem. In this approach, a set of concepts instead of keywords will be extracted from a query first. Then these concepts will be used for retrieval purpose. The experiment results show that the proposed approach can boost the retrieval performance and it generates very good results on 2005 TREC Genomics data sets.
Ming Zhong 0013, Jimmy Huang 0001
 SIGIR1