Working with Text - 1st Edition - ISBN: 9781843347491, 9781780634302

Working with Text

1st Edition

Tools, Techniques and Approaches for Text Mining

Authors: Emma Tonkin Gregory Tourte
eBook ISBN: 9781780634302
Paperback ISBN: 9781843347491
Imprint: Chandos Publishing
Published Date: 30th April 2014
Page Count: 344
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Table of Contents

  • Preface
  • Acknowledgements
  • Chapter 1: Working with Text
    • 1.1 Introduction: Portraits of the Past
    • 1.2 The Reading Robot
    • 1.3 From Data to Text Mining
    • 1.4 Definitions of Text Mining
    • 1.5 Exploring the Disciplinary Neighbourhood
    • 1.6 Prerequisites for Text Mining
    • 1.7 Learning Minecraft: What Makes a Text Miner?
    • 1.8 Contemporary Attitudes to Text Mining
    • 1.9 Conclusions
  • Chapter 2: A Day at Work (with Text): A Brief Introduction
    • Abstract
    • 2.1 Introduction
    • 2.2 Encouraging an Interest in Text Mining
    • 2.3 Legal and Ethical Aspects of Text Mining
    • 2.4 Manual Annotation: Preparing for Evaluation
    • 2.5 Common Text Mining Tasks
    • 2.6 Basic Corpus Analysis
    • 2.7 Preprocessing a Text
    • 2.8 Extracting Features from a Text
    • 2.9 Information Extraction
    • 2.10 Applications of Indexing and Metadata Extraction
    • 2.11 Extraction of Subjective Views
    • 2.12 Build, Customise or Apply? Choosing an Appropriate Implementation
    • 2.13 Evaluation
    • 2.14 The Role of Visualisation in Text Mining
    • 2.15 Visualisation Tools and Frameworks
    • 2.16 Conclusions
  • Chapter 3: If You Find Yourself in a Hole, Stop Digging: Legal and Ethical Issues of Text/Data Mining in Research
    • Abstract
    • 3.1 Introduction
    • 3.2 Key Legal Issues in Data Mining
    • 3.3 Ethics
    • 3.4 Conclusions: Working on the Borders of Law and Ethics
  • Chapter 4: Responsible Content Mining
    • Abstract
    • 4.1 Introduction to Content Mining
    • 4.2 Obtaining Permission to Content Mine
    • 4.3 Responsible Crawling
    • 4.4 Publication of Results
    • 4.5 Citation and Acknowledgement
    • 4.6 Proposed Best Practise Guidelines for Content Mining
  • Chapter 5: Text Mining for Semantic Search in Europe PubMed Central Labs
    • Abstract
    • 5.1 Introduction
    • 5.2 Previous Work
    • 5.3 Design and Implementation
    • 5.4 Performance and Critique
    • 5.5 Conclusions
    • 5.6 Availability
    • Appendix: Resources Used for Indexing
  • Chapter 6: Extracting Information from Social Media with GATE
    • Abstract
    • Acknowledgements
    • 6.1 Introduction
    • 6.2 Social Media Streams: Characteristics, Challenges and Opportunities
    • 6.3 The GATE Family of Text Mining Tools: An Overview
    • 6.4 Information Extraction: An Overview
    • 6.5 IE from Social Media with GATE
    • 6.6 Conclusion and Future Work
  • Chapter 7: Newton: Building an Authority-Driven Company Tagging and Resolution System
    • Abstract
    • Acknowledgements
    • 7.1 Introduction
    • 7.2 Related Work
    • 7.3 System Overview
    • 7.4 Learning Company Name Links
    • 7.5 System Development
    • 7.6 Conclusions
  • Chapter 8: Automatic Language Identification
    • Abstract
    • Acknowledgements
    • 8.1 Introduction
    • 8.2 Historical Overview
    • 8.3 Computational Techniques
    • 8.4 Applications and Related Tasks
    • 8.5 Conclusion
  • Chapter 9: User-Driven Text Mining of Historical Text
    • Abstract
    • Acknowledgements
    • 9.1 Related Work on Text Mining Historical Documents
    • 9.2 The Trading Consequences System
    • 9.3 Data Collections
    • 9.4 Challenges of Processing Digitised Historical Text
    • 9.5 Text Mining Component
    • 9.6 User-Driven Text Mining
    • 9.7 Conclusion
  • Chapter 10: Automatic Text Indexing with SKOS Vocabularies in HIVE
    • Abstract
    • Acknowledgements
    • 10.1 Introduction
    • 10.2 Automatic Indexing with Machine Learning
    • 10.3 Algorithms for Text Data Mining: KEA, KEA++ and MAUI
    • 10.4 Algorithm Training and Workflow
    • 10.5 The HIVE System
    • 10.6 Text Mining for Documents Indexing Using SKOS Vocabularies in HIVE
    • 10.7 Conclusions
  • Chapter 11: The PIMMS Project and Natural Language Processing for Climate Science: Extending the ChemicalTagger Natural Language Processing Tool with Climate Science Controlled Vocabularies
    • Abstract
    • Acknowledgements
    • 11.1 Introduction
    • 11.2 Methodology
    • 11.3 Results
    • 11.4 Overall Conclusions and Suggestions for Further Work
  • Chapter 12: Building Better Mousetraps: A Linguist in NLP
  • Chapter 13: Raúl Garreta, Co-founder of Tryolabs.com, Tells Emma Tonkin About the Journey from Software Engineering Graduate to Startup Entrepreneur
  • Appendix A: Resources for Text Mining
    • A.1 Introduction
    • A.2 Text Mining Software and Libraries
    • A.3 Text Mining Frameworks and Packages
    • A.4 Web Mining Packages
    • A.5 Data Mining Packages
    • A.6 A Selection of Components and Packages
    • A.7 Web Interfaces for Text Mining
    • A.8 Distribution and Scaling
  • Appendix B: Databases and Vocabularies
    • B.1 Sample Data Sets
    • B.2 Datasets primarily used for text categorization
    • Sources
    • Uses
    • B.3 Useful Tertiary Data Sets
    • Sources
  • Appendix C: Visualisation Tools and Resources
    • C.1 D3 – Data Driven Documents
    • C.2 Processing and Processing.js
    • C.3 Map Display
    • C.4 Command Line Visualisation Tools
    • C.5 Graphical Tools
    • C.6 Geographic Data Sets
  • Appendix D: Learning Opportunities
    • D.1 United Kingdom
    • D.2 Ireland
    • D.3 Sweden
    • D.4 France
    • D.5 United States
    • D.6 Short Courses, Training Courses and MOOCs
  • Index

Description

What is text mining, and how can it be used? What relevance do these methods have to everyday work in information science and the digital humanities? How does one develop competences in text mining? Working with Text provides a series of cross-disciplinary perspectives on text mining and its applications. As text mining raises legal and ethical issues, the legal background of text mining and the responsibilities of the engineer are discussed in this book. Chapters provide an introduction to the use of the popular GATE text mining package with data drawn from social media, the use of text mining to support semantic search, the development of an authority system to support content tagging, and recent techniques in automatic language evaluation. Focused studies describe text mining on historical texts, automated indexing using constrained vocabularies, and the use of natural language processing to explore the climate science literature. Interviews are included that offer a glimpse into the real-life experience of working within commercial and academic text mining.

Key Features

  • Introduces text analysis and text mining tools
  • Provides a comprehensive overview of costs and benefits
  • Introduces the topic, making it accessible to a general audience in a variety of fields, including examples from biology, chemistry, sociology, and criminology

Readership

Students, Researchers and Information professionals


Details

No. of pages:
344
Language:
English
Copyright:
© Chandos Publishing 2016
Published:
Imprint:
Chandos Publishing
eBook ISBN:
9781780634302
Paperback ISBN:
9781843347491

About the Authors

Emma Tonkin Author

Emma Tonkin is a Senior Research Associate in the Faculty of Engineering at the University of Bristol. She has held positions in several universities, having previously worked in Digital Library research at UKOLN, University of Bath, and in the Department of Digital Humanities, King’s College, London. She holds a PhD in Computer Science from the University of Bristol. Her primary research interests include text and data mining, human computer interaction and the development of hybrid systems that combine human and machine classification

Affiliations and Expertise

Senior Research Associate, Faculty of Engineering, University of Bristol, UK

Gregory Tourte Author

Gregory Tourte is Senior Research Associate at the University of Bristol in the School of Geographical Sciences where he started as a system administrator for the research group’s supercomputer and used this opportunity to research data management due to the large quantity of data being generated. He continues his work with deep time climate modelling within the Bristol Research Initiative for the Dynamic Global Environment (BRIDGE).

Affiliations and Expertise

Senior Research Associate, School of Geographical Sciences, University of Bristol, UK