Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.

从文本中提取内容仍然是信息处理和管理的一个重要研究问题。捕捉基于文本的文件集的语义的方法可能是基于贝叶斯模型、概率论、向量空间模型、统计模型，甚至是图论。随着数字化文本媒体的数量不断增加，设计强大的、可扩展的索引和搜索策略（软件）以满足各种用户需求的需求也在不断增加。从文本中提取或创造知识需要系统而可靠的处理方法，这种处理方法可以被编纂并适应不断变化的需求和环境。本书将借鉴学术界和工业界的专家，推荐净化、索引和挖掘文本信息的实用方法。它将讨论文件识别、文件聚类和分类、清理文本以及文本语义模型的可视化。