本文的研究着重于知识发现在通信行业客户服务文本记录分类这一问题的应用上。
The study of this text emphasizes on the application of knowledge discovery in the classification of textual records on the customer service in the communication industry.
文本挖掘是基于非相关文献知识发现的核心。
Text mining is the kernel of the disjoint literature-based knowledge discovery.
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 latest researches of biomedical literature knowledge discovery, including main issues, accomplishment, and the key methods from text mining perspective, are discussed.
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。
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.
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.
本文在最后还探讨了自动文摘在“知识发现”和文本信息挖掘领域内的初步应用。
In the end, the author discusses the application of automatic abstracting in the KDD (Knowledge Discovery from the Data) field.
SAS的文本矿工提供丰富的工具套件，用于发现并从文本文件中提取知识。
SAS text Miner provides a rich suite of tools for discovering and extracting knowledge from text documents.
SAS的文本矿工提供丰富的工具套件，用于发现并从文本文件中提取知识。
SAS text Miner provides a rich suite of tools for discovering and extracting knowledge from text documents.
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