Text Mining: Predictive Methods for Analyzing Unstructured Information (Paperback)

The growth of the web can be seen as an expanding public digital library collection. Online digital information extends far beyond the web and its publicly available information. Huge amounts of information are private and are of interest to local communities, such as the records of customers of a business. This information is overwhelmingly text and has its record-keeping purpose, but an automated analysis might be desirable to find patterns in the stored records. Analogous to this data mining is text mining, which also finds patterns and trends in information samples but which does so with far less structured--though with greater immediate utility for users--ingredients. This book focuses on the concepts and methods needed to expand horizons beyond structured, numeric data to automated mining of text samples. It introduces the new world of text mining and examines proven methods for various critical text-mining tasks, such as automated document indexing and information retrieval and search. New research areas are explored, such as information extraction and document summarization, that rely on evolving text-mining techniques.

網絡的增長可以被視為一個不斷擴大的公共數字圖書館收藏。在線數字信息遠不止於網絡及其公開可用的信息。大量的信息是私有的，對於地方社區來說具有興趣，例如一家企業的客戶記錄。這些信息主要是文本形式，具有記錄目的，但可能需要自動分析以找出存儲記錄中的模式。類似於數據挖掘的是文本挖掘，它也可以在信息樣本中找到模式和趨勢，但它所使用的結構較少，對用戶來說具有更大的即時效用。本書專注於擴展視野，從結構化的數字數據轉向自動化挖掘文本樣本。它介紹了文本挖掘的新世界，並探討了各種關鍵的文本挖掘任務所需的概念和方法，例如自動文檔索引和信息檢索和搜索。還探討了依賴不斷發展的文本挖掘技術的信息提取和文檔摘要等新的研究領域。