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A Mixture Language Model for Class-Attribute Mining from Biomedical Literature Digital Library

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成果类型:
期刊论文
作者:
Zhou, Xiaohua*;Hu, Xiahoua;Zhang, Xiaohua;Wu, Daniel D.;He, Tingting(何婷婷);...
通讯作者:
Zhou, Xiaohua
作者机构:
[Hu, Xiahoua; Wu, Daniel D.; Zhou, Xiaohua; Zhang, Xiaohua] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.
[He, Tingting] Cent China Normal Univ, Wuhan, Peoples R China.
[Luo, Aijing] Cent S Univ, Changsha, Hunan, Peoples R China.
通讯机构:
[Zhou, Xiaohua] D
Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.
语种:
英文
期刊:
2008 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, PROCEEDINGS
ISSN:
2156-1125
年:
2008
页码:
17-22
基金类别:
NSF Career Grant [IIS 0448023]; NSF [CCF 0514679]; PA Dept of Health Tobacco Settlement Formula Grant [240205, 240196]; PA Dept of Health [239667]; Programme of Introducing Talents of Discipline to Universities [B07042]
机构署名:
本校为其他机构
摘要:
We define and study a novel text mining problem for biomedical literature digital library, referred to as the class-attribute mining. Given a collection of biomedical literature from a digital library addressing a set of objects (e.g., proteins) and their descriptions (e.g., protein functions), the tasks of class-attribute mining include: (1) to identify and summarize latent classes in the space of objects, (2) to discover latent attribute themes in the space of object descriptions, and (3) to summarize the commonalities and differences among i...

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