Grant Ingersoll is an independent consultant developing search and natural language processing tools. Prior to being a consultant, he was a Senior Software Engineer at the Center for Natural Language Processing at Syracuse University with 11 years of hands-on experience developing Java applications, many of which have been spent working on text processing applications. At the Center and, previously, at MNIS-TextWise, Grant 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. He holds a master's degree in computer science from Syracuse University and a bachelor's degree in mathematics and computer science from Amherst College.

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. He received his doctorate in Computer Science from the University of Pennsylvania in 2005, and has worked in several industry positions applying text processing and machine learning to enterprise class development efforts. Currently he works as a software architect for Comcast Interactive Media in Philadelphia.

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 software engineers who want to make their text-based applications more useful and user-friendly. Whether you're 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 a daunting task.

Taming Text is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. This book explores how to automatically organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. The book guides you through examples illustrating each of these topics, as well as the foundations upon which they are bulit.