文本知识发现：基于信息抽取的文本挖掘

1.引言大家熟知,所谓“数据丰富但知识缺乏“的现状导致了数据挖掘(Data Mining)技术研究的兴起,数据挖掘又称数据库知识发现(Knowledge Discovery in Databases)是从海量的结构化信息中抽取或挖掘隐含信息和知识的重要方法和途径。数据挖掘技术已相当成熟。因为除了结构化的数据之外,在数字化信息中更多地存在大量自由、非结构化或半结构化的文本信息如新闻文章、电子书本、电子图书馆藏、Web页面内容、Email、文档数据库等,显然手工处理需要花费大量的人力物力,并且具有不确定性。所以出现了从文本中发现知

Knowledge Discovery in Text: A Survey

In the general context of Knowledge Discovery, Knowledge Discovery in Text (KDT), which uses Text Mining techniques to extract and induce hidden knowledge from unstructured text data, surges in the data and natural language processing research. KDT is a multi-discipline of Artificial Intelligence, Machine learning, Natural Language Processing and Information Extraction & Information Retrieval. This paper presents an overview of Text Mining with a stressing on its IE (Information Extraction)-based induction and specific sublanguage fields oriented practices.