Text Mining for Biology And Biomedicine Review

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Text Mining for Biology And Biomedicine ReviewThis book was written to help biologists manage the flood of literature in their profession. Online search tools help some, but not enough. Text analysis, "the process of discovering and extracting knowledge from unstructured data," can help with this literature tsunami. Biologists--and researchers in related disciplines--can use the tools and techniques of text analysis to identify relevant documents, extract useful information from them, and discover new relationships between these hard-won nuggets.
Sophe Ananiadou and John McNaught have assembled a collection of chapters written by experienced text analysts. They cover the primary subareas of text analysis with little overlap. These chapters are well-documented, use examples from biology, and explain them well enough to make the book accessible to non-biologists. After a concise introductory outline in the first chapter, readers become familiar with the general techniques of natural language processing, the various research communities using NLP techniques, and the tools and other resources they have created. Subsequent chapters cover terminology management, abbreviations, named entities (think "proper nouns"), information extraction, and corpus tagging. The final two chapters discuss how text analysis techniques are evaluated and how results are integrated into structured data analysis.
The clear explanations and accessible examples make this book a reasonable first read for anyone curious about text analysis. Although readers need not be biologists, some familiarity with scientific inquiry is helpful. I found the chapter on evaluation of text analysis methods most useful. It explains the role of challenge competitions in advancing the field and describes the common evaluation metrics well enough to facilitate understanding of the research literature. All chapters include numerous web links to software tools, data sources, and relevant research groups. This is a particular strength of the book, and helps readers bridge the gap between text analysis as it was in 2005, and as it has become since the book's publication.
It is recommended for scientists who want to learn about text analysis by observing it's practical applications in biology. Readers preferring a "straight" introduction without the biology might turn to The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data or the last chapter in Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management.Text Mining for Biology And Biomedicine OverviewWith the volume of biomedical research growing exponentially worldwide, the demand for information retrieval expertise in the field has never been greater. Here's the first guide for bioinformatics practitioners that puts the full range of biological text mining tools and techniques at their fingertips in a single dedicated volume. It describes the methods of natural language processing (NLP) and their applications in the biological domain, and spells out the various lexical, terminological, and ontological resources at their disposal - and how best to utilize them. Readers see how terminology management tools like term extraction and term structuring facilitate effective mining, and learn ways to readily identify biomedical named entities and abbreviations. The book explains how to deploy various information extraction methods for biological applications. It helps professionals evaluate and optimize text-mining systems, and includes techniques for integrating text mining and data mining efforts to further facilitate biological analyses.

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The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data Review

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The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data ReviewThis was one of the few books that included a very clear and extensive treatment of information extraction techniques. There were plenty of diagrams which is great for a visual learner. All the techniques are explained using both plain English and formulas, so that you can pick up the scientific notation with minimal previous knowledge.
Even when the authors plug their own company and research at the end it was moderately useful in illustrating the concepts mentioned in a real world scenario.The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data OverviewText mining tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, this book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, it explores current real-world, mission-critical applications of text mining and link detection in such varied fields as M&A business intelligence, genomics research and counter-terrorism activities.

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Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies Review

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Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies ReviewQuite introductory material, good for beginners (students as well as teachers planning a course) and strong in showing the links between critical discourse analysis and corpus linguistics.Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies OverviewIntroducing Electronic Text Analysis is a practical and much needed introduction to corpora-bodies of linguistic data. Written specifically for students studying this topic for the first time, the book begins with a discussion of the underlying principles of electronic text analysis. It then examines how these corpora enhance our understanding of literary and non-literary works. In the first section the author introduces the concepts of concordance and lexical frequency, concepts whichare then applied to a range of areas of language study. Key areas examined are the use of on-line corpora to complement traditional stylistic analysis, and the ways in which methods such as concordance and frequency counts can reveal a particular ideology within a text. Presenting an accessible and thorough understanding of the underlying principles of electronic text analysis, the book contains abundant illustrative examples and a glossary with definitions of main concepts. Itwill also besupported by a companion website with links to on-line corpora so that students can apply their knowledge to further study. The accompanying website to this book can be found at http://www.routledge.com/textbooks/0415320216

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Handbook of Statistical Analysis and Data Mining Applications Review

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Handbook of Statistical Analysis and Data Mining Applications ReviewThe "Handbook of Statistical Analysis & Data Mining Applications" is the finest book I have seen on the subject. It is not only a beautifully crafted book, with numerous color graphs, chart, tables, and screen shots, but the statistical discussion is both clear and comprehensive.
The text does not use only one statistical data mining application to display examples, but provides a rather thorough training in the use of both SAS-Enterprise Miner and STATISTICA Data Miner. A section on SPSS Clementine is also provided, giving comparisons between the various packages. Also employed are STATISTICA's C&RT, CHAID, MARSpline, and other data mining and graphical analytic tools.
The text does not burden the typical data mining researcher with the internals of how the various tools work. It is therefore not steeped in equations. Some are to be found, of course, but the emphasis is on understanding the concepts involved and on how to apply these concepts to real data - which is provided to the reader in terms of data tutorials. Specialized datasets have been prepared by both authors and outside experts in various areas of inquiry ranging from entertainment, financial, engineering, clinical psychology, dentistry, demographics, medical informatics, meteorology, astronomy, and more. Each tutorial is associated with data stored on either the associated CD that comes with the book, or which can be downloaded from a companion web site. Worked out examples of how to use data mining techniques on such data is provided to help the reader gain a solid feel for the data mining enterprise. The final third of the book is devoted to a partial selection of the available tutorials. The two earlier chapters demonstrate how to use data mining software for the analysis of data.
I highly recommend this work to anyone having an interest in data mining. I might also add that the Amazon price of $72.37 is truly excellent for an 864 page academic text, having full color tables and screen shots on some one-third of the pages, plus a CD. A bargain indeed.
Handbook of Statistical Analysis and Data Mining Applications OverviewThe Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.

Written "By Practitioners for Practitioners"
Non-technical explanations build understanding without jargon and equations
Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models using Statistica, SAS and SPSS software
Practical advice from successful real-world implementations
Includes extensive case studies, examples, MS PowerPoint slides and datasets
CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book


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Introduction to Information Retrieval Review

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Introduction to Information Retrieval ReviewI am a big fan of the authors 1999 book on Statistical Natural Language Processing, and I and was thrilled when I found this new book online -- just search for "Information Retrieval" on Google.
In these two books, they describe the theory behind a vast toolbox which can be used to construct new tools/products for the Internet. Now I can go back to them when the need arises.
For starters, I appreciate the detailed theoretical explanations of topics that I could not find in other texts, and the references to related work are especially helpful. One of the other books I read was Information Retrieval by Grossman, which is an older book but has a more condensed style compared to this. Grossman's discussion of clustering was more high level and referenced a few more papers that I found useful. That helped increase my interest to read through these chapters in which offer greater detail.
Before I felt like I could place each topic in its appropriate context, I had to spend six months of reading both the books, playing with code and finding s/w packages, searching the research literature, reading papers and other books, and then cycling back to the books. Here's are some suggestions for things I'd like to see:
1. A set of recomended programming tools: in some books on Perl -- such as the chapter "Natural Language Tools" in pages 149-171 in "Advanced Perl Programming" by Simon Cozens (O'Reilly) -- you get a very "quick & dirty" introduction to maybe 20-30% of the concepts in these two books along with ways to implement and play around with them. Although Perl has many natural language processing tools, the Cozens book cuts to the chase, explains which are the best tools, and shows you how to use them. I think knowing such shortcuts aids in learning how to apply and improve on them. The more complex and sophisticated topics, the more likely to make it out into the real world if they are easy to play with.
2. More data/examples on what does/doesn't work with end-users: Numbers, graphs, and charts are all good stuff. I always appreciate it when the authors referenced quantitative comparisons, real-world products, and history of Internet. One of the reasons I had to consult the research literature was to broaden my understanding of quantitative comparisons between different techniques involving end-users, which were typically done in the context of complete systems studies that users could try out.
Thanks,
-SriIntroduction to Information Retrieval OverviewClass-tested and coherent, this groundbreaking new textbook teaches web-era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

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