By Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred Damerau
One end result of the pervasive use of desktops is that the majority records originate in electronic shape. textual content mining—the technique of looking, retrieving, and examining unstructured, natural-language text—is excited about tips to make the most the textual information embedded in those documents.
Text Mining offers a finished advent and assessment of the sphere, integrating similar themes (such as man made intelligence and information discovery and knowledge mining) and delivering sensible suggestion on how readers can use text-mining the right way to study their very own info. Emphasizing predictive tools, the booklet unifies all key parts in textual content mining: preprocessing, textual content categorization, info seek and retrieval, clustering of files, and knowledge extraction. moreover, it identifies rising instructions for these seeking to do study within the region. a few historical past in info mining is useful, yet no longer essential.
Topics and features:
* provides a complete and easy-to-read advent to textual content mining
* Explores the applying and application of the equipment, in addition to the optimum suggestions for particular situations
* presents numerous descriptive case reviews that take readers from challenge description to procedure deployment within the genuine world
* makes use of tools that depend upon easy statistical innovations, therefore bearing in mind relevance to all languages (not simply English)
* contains entry to downloadable software program (runs on any computer), in addition to valuable chapter-ending historic and bibliographical feedback, an in depth bibliography, and topic and writer indexes
This authoritative and hugely available textual content, written by way of a crew of specialists on textual content mining, develops the basis strategies, ideas, and techniques had to extend past dependent, numeric info to computerized mining of textual content samples. Researchers, computing device scientists, and complex undergraduates and graduates with paintings and pursuits in facts mining, laptop studying, databases, and computational linguistics will locate the paintings a vital resource.
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Additional resources for Text Mining: Predictive Methods for Analyzing Unstructured Information
Proﬁts yes yes ... ... ... increased yes no ... ... ... earnings yes yes ... ... ... stock-price 1 0 ... 3. Abstract Spreadsheet for Predicting Stock Price about companies, and the labels are whether the stock price rose in some time period following the article. So far, we have not shied away from describing text as unstructured data that can be converted into structured data, where classical machine-learning methods can be applied. There remain many nuances in the recipe that do not alter this worldview but can make our trip to obtaining good results more direct.
Formulated in this way, the phrase identiﬁcation problem is reduced to a classiﬁcation problem for the tokens of a sentence, in which the procedure must supply the correct class for each token. Performance varies widely over phrase type, although overall performance measures on benchmark test sets are quite good. A simple statistical approach to recognizing signiﬁcant phrases might be to consider multiword tokens. If a particular sequence of words occurs frequently enough in the corpora, it will be identiﬁed as a useful token.
A special issue of the Journal of Machine Learning Research, in 2003 was devoted to feature selection and is available online. One of the papers [Forman, 2003] presents experiments on various methods for feature reduction. A useful reference on word selection methods for dimensionality reduction is [Yang and Pedersen, 1997], which discusses a wide variety 46 2. From Textual Information to Numerical Vectors of methods for selecting words useful in categorization. It concludes that document frequency is comparable in performance to expensive methods such as information gain or chi-square.