Text mining is data mining applied to information extracted from text.
文本挖掘就是用于从文本中提取信息的数据挖掘技术。
Text categorization is an important task in Data Mining, and it is an important way for getting information.
文本分类是数据挖掘的重要课题，它是获取信息资源的重要方式之一。
Since the text is one of the most widely used methods in storing information, it has great meaning on text mining.
由于存储信息最多的自然形式就是文本，因此文本挖掘具有重要的意义。
Recently data mining and text mining are important research areas in information technology.
数据挖掘和文本挖掘是当前信息技术中的一个重要研究领域；
However, conventional text mining technology cannot achieve high accuracy, because it cannot effectively make use of the semantic information of the text.
传统的文本挖掘方法由于不能有效运用语义信息而难以达到更高的准确度。
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。
In this context, text mining has become extremely prevalent, giving rise to an age where vast amounts of textual information can be accessed, analyzed, and processed in a fraction of a second.
在这种状况下，文本挖掘已经成为极为普遍，从而引发大量的文字信息，可以访问的年龄，分析，并在几分之一秒解决。
Absrtact: Text classification is the base of information retrieval and data mining and it is widely used in web data mining and search engine.
摘要：文本分类是信息检索和数据挖掘的基础，被广泛应用于网络数据挖掘及搜索引擎等方面。
The connotation of theory of body and sprit is copious and text mining can find potential valuable information from plenty of message.
本研究试图运用文本挖掘技术对形神理论的内涵进行挖掘。
Foreign Text mining and data visualization tools in patent information analysis.
专利文本挖掘和可视化工具研究。
It's the first mining solution that tightly integrates text-based information with structured data for improved analyses and decision making.
它第一次开发出把文本信息和结构化数据紧密集成的解决方案，以提高对数据的分析和决策能力。
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。
应用推荐