Text Mining: From Ontology Learning to Automated Text Processing

In this review of Text Mining: From Ontology Learning to Automated Text Processing Applications, the authors present a focused, academic treatment of methods for extracting knowledge from large text corpora. The book is best for researchers and advanced practitioners who need a methodical overview of how text mining supports the creation of lexical resources such as dictionaries and ontologies and how those resources power automated text processing in domains like history and healthcare. The single biggest reason to buy is its systematic coverage of methodology that links corpus analysis to practical resource building and application.

Key Features

• Methodology-focused: The book lays out reproducible text mining approaches so readers can apply the same workflows to build lexical resources from raw corpora.
• Lexical resource creation: Detailed coverage explains how large dictionaries and ontologies are automatically generated from corpus analysis, useful for downstream NLP tasks.
• Application breadth: Examples show how text mining supports automated processing in fields such as history, healthcare and mobile applications, illustrating cross-domain utility.
• Updated techniques: The volume includes recent advances in text mining methods, helping practitioners stay current with evolving automated processing approaches.
• Research orientation: Chapters are written for readers already performing text mining, offering depth rather than introductory overview for better experimental use.

Who It's For

This book is aimed at researchers, graduate students and developers who already have some background in natural language processing and want to deepen their practical knowledge of text mining methodology and resource construction. It is particularly relevant for teams building lexical resources, ontologies or automated pipelines that rely on large-scale corpus analysis.

Readers seeking a beginner-friendly introduction to NLP or a hands-on programming tutorial with code snippets and step-by-step exercises should look elsewhere, since the volume emphasizes methodology, theoretical framing and case studies over beginner tutorials.

Pros & Cons

Pros

• Comprehensive methodology that connects corpus analysis to explicit steps for building lexical resources.
• Practical examples across domains illustrate how text mining yields usable automated processing applications.
• Updated discussion of recent advances keeps readers informed about current approaches in the field.

Cons

• Not a beginner primer; assumes reader familiarity with NLP concepts and prior text mining experience.

Specifications

Title	Text Mining: From Ontology Learning to Automated Text Processing Applications
Series	Theory and Applications of Natural Language Processing
Editors / Authors	Chris Biemann, Alexander Mehler
Focus	Methodology of text mining and automatic lexical resource construction
Applications discussed	History, healthcare, mobile applications and other automated text processing uses
Audience	Researchers and practitioners in NLP and text mining

Our Verdict

This volume is a valuable methodological reference for anyone serious about using corpus analysis to build lexical resources and deploy automated text processing. It offers depth and domain-relevant examples that make it good value for researchers and experienced practitioners, though those new to NLP may find the material assumes prior knowledge.

Frequently Asked Questions

Does this book include practical examples?
The book presents domain examples and case studies to illustrate methods, but it focuses on methodology rather than step-by-step programming tutorials.

Is this title suitable for beginners?
It is primarily aimed at researchers and practitioners with existing NLP experience; beginners should supplement it with introductory resources.

What kinds of applications are covered?
Applications include automated text processing in areas such as history, healthcare and mobile applications, showing how lexical resources support real tasks.