Table of Contents:
  • 1. Introduction
  • 1.1 Historical languages and modern languages
  • 1.2 Intended audience
  • 1.3 Outline
  • 2. NLP and digital humanities
  • 2.1 Origins of digital humanities
  • 2.2 Convergence of NLP and digital humanities
  • 2.3 Summary
  • 3. Spelling in historical texts
  • 3.1 The role of orthography in NLP
  • 3.2 Spelling and historical texts
  • 3.3 Summary
  • 4. Acquiring historical texts
  • 4.1 Digitization of historical texts
  • 4.2 Scanning
  • 4.3 Optical character recognition
  • 4.4 Manual text entry
  • 4.5 Computer-assisted transcription
  • 4.6 Summary
  • 5. Text encoding and annotation schemes
  • 5.1 Unicode for historical text
  • 5.2 TEI for historical texts
  • 5.3 Summary
  • 6. Handling spelling variation
  • 6.1 Spelling canonicalization
  • 6.2 Edit distance
  • 6.3 Approaches for handling spelling variation in historical texts
  • 6.4 Detecting and correcting OCR errors
  • 6.5 Limits of spelling canonicalization
  • 6.6 Summary
  • 7. NLP tools for historical languages
  • 7.1 Part-of-speech tagging
  • 7.2 Lemmatization and morphological analysis
  • 7.3 Syntactic parsing
  • 7.4 Summary
  • 8. Historical corpora
  • 8.1 Arabic
  • 8.2 Chinese
  • 8.3 Dutch
  • 8.4 English
  • 8.5 French
  • 8.6 German
  • 8.7 Nordic languages
  • 8.8 Latin and ancient Greek
  • 8.9 Portuguese
  • 8.10 Summary
  • 9. Conclusion
  • Bibliography
  • Author's biography.