Natural Language Processing and Text Mining by Anne KaoNatural Language Processing and Text Mining by Anne Kao

Natural Language Processing and Text Mining

EditorAnne Kao, Steve R. Poteet

Paperback | October 13, 2010

Pricing and Purchase Info

$181.71 online 
$193.95 list price save 6%
Earn 909 plum® points

Prices and offers may vary in store

Quantity:

In stock online

Ships free on orders over $25

Not available in stores

about

With the increasing importance of the Web and other text-heavy application areas, the demands for and interest in both text mining and natural language processing (NLP) have been rising. Researchers in text mining have hoped that NLP-the attempt to extract a fuller meaning representation from free text-can provide useful improvements to text mining applications of all kinds.Bringing together a variety of perspectives from internationally renowned researchers, Natural Language Processing and Text Mining not only discusses applications of certain NLP techniques to certain Text Mining tasks, but also the converse, i.e., use of Text Mining to facilitate NLP. It explores a variety of real-world applications of NLP and text-mining algorithms in comprehensive detail, placing emphasis on the description of end-to-end solutions to real problems, and detailing the associated difficulties that must be resolved before the algorithm can be applied and its full benefits realized. In addition, it explores a number of cutting-edge techniques and approaches, as well as novel ways of integrating various technologies. Nevertheless, even readers with only a basic knowledge of data mining or text mining will benefit from the many illustrative examples and solutions.Topics and features:. Describes novel and high-impact text mining and/or natural language applications. Points out typical traps in trying to apply NLP to text mining. Illustrates preparation and preprocessing of text data - offering practical issues and examples. Surveys related supporting techniques, problem types, and potential technique enhancements. Examines the interaction of text mining and NLPThis state-of-the-art, practical volume will be an essential resource for professionals and researchers who wish to learn how to apply text mining and language processing techniques to real world problems. In addition, it can be used as a supplementary text for advanced students studying text mining and NLP.
Title:Natural Language Processing and Text MiningFormat:PaperbackDimensions:277 pages, 9.25 × 6.1 × 0 inPublished:October 13, 2010Publisher:Springer LondonLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:1849965587

ISBN - 13:9781849965583

Look for similar items by category:

Reviews

Table of Contents

Overview.- Extracting Product Features and Opinions from Reviews.- Extracting Relations from Text.- Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles.- A Case Study in Natural Language Based Web Search.- Evaluating Self-explanations in iSTART:Word Matching, Latent Semantic Analysis, and Topic Models.- Textual Signatures: Identifying Text-Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures.- Automatic Document Separation - A Combination of Probabilistic Classification and Finite-State Sequence Modeling.- Evolving Explanatory Novel Patterns for Semantically-based Text Mining.- Handling of Imbalanced Data in Text Classification: Category Based Term Weights.- Automatic Evaluation of Ontologies.- Linguistic Computing with UNIX Tools.- Index

Editorial Reviews

From the reviews:"The papers in this book describe a range of natural language processing (NLP) techniques and applications, all originating from an ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) panel discussion. . Overall, the contributions are well balanced with respect to the different approaches presented . . The volume appears to serve its intended purpose, which is to provide an electric overview of the international research efforts in text mining, featuring relevant tools and techniques from NLP and machine learning." (Peter Jackson, Computing Reviews, March, 2008)