Text mining is the kernel of the disjoint literature-based knowledge discovery.
文本挖掘是基于非相关文献知识发现的核心。
Relation extraction is an important task in text mining, it can reflect the relationship between the named entities and is helpful to find implicit knowledge in the substantial data and text.
关系抽取是文本挖掘的一项重要研究内容，它能够反映命名实体之间的关系，有助于发现隐含在大量数据和文本中的知识。
Text mining is an effective means of detecting potentially useful knowledge from large text database.
文本挖掘技术是从海量文本信息中获取潜在有用知识的有效途径。
The latest researches of biomedical literature knowledge discovery, including main issues, accomplishment, and the key methods from text mining perspective, are discussed.
本文对生物医学文献知识发现的研究内容、研究成果以及基于文本挖掘的关键技术诸方面进行了系统的分析和阐述。
The content characteristics and outer characteristics representing information comprise the basis for the revelation of text knowledge correlation and knowledge mining.
能够体现信息的内容特征和外表特征共同构成了文本知识关联揭示和知识挖掘的基础。
Research Interests: Text Mining, Knowledge Discovery FROM Text, Collaborative Technologies, Medical Informatics, User Information Behaviors, Information Synthesis.
研究方向：文本挖掘，文字的知识发现，协同技术，医学信息，用户信息的行为，信息综合。
Research Interests: Text Mining, Knowledge Discovery FROM Text, Collaborative Technologies, Medical Informatics, User Information Behaviors, Information Synthesis.
研究方向：文本挖掘，文字的知识发现，协同技术，医学信息，用户信息的行为，信息综合。
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