Taming Text How to Find, Organize and Manipulate it
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Taming Text How to Find, Organize and Manipulate it

By (author) Grant S. Ingersoll , By (author) Thomas S. Morton , By (author) Andrew L. Farris

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DESCRIPTION It is no secret that the world is drowning in text and data. This causes real problems for everyday users who need to make sense of all the information available, and for software engineers who want to make their text-based applications more useful and user-friendly. Whether building a search engine for a corporate website, automatically organizing email, or extracting important nuggets of information from the news, dealing with unstructured text can be daunting. Taming Text is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. It explores how to automatically organize text, using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. This book gives examples illustrating each of these topics, as well as the foundations upon which they are built. KEY POINTS `h One-stop shop for learning how to process text `h Clear, concise, and practical advice `h Builds on high quality open source libraries

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  • Paperback | 298 pages
  • 189 x 234 x 17mm | 538g
  • 31 Jan 2013
  • Manning Publications
  • New York
  • English
  • 193398838X
  • 9781933988382
  • 222,534

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Author Information

Grant Ingersoll is an independent consultant developing search and natural language processing tools. He has worked on a number of text processing applications involving information retrieval, question answering, clustering, summarization, and categorization. Grant is a committer, as well as a speaker and trainer, on the Apache Lucene Java project and a co-founder of the Apache Mahout machine-learning project. Thomas Morton writes software and performs research in the area of text processing and machine learning. He has been the primary developer and maintainer of the OpenNLP text processing project and Maximum Entropy machine learning project for the last 5 years. Currently, he works as a software architect for Comcast Interactive Media in Philadelphia. Drew Farris is a professional software developer and technology consultant whose interests focus on large scale analytics, distributed computing and machine learning. He has contributed to a number of open source projects including Apache Mahout, Lucene and Solr, and holds a master's degree in Information Resource Management from Syracuse University's iSchool and a B.F.A in Computer Graphics.

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