Contents

• Contents
• What is Data-Intensive Linguistics?
  • Introduction
    • Aims of the book
    • Recommended reading
    • Prerequisites
    • Chapters
    • The heart of our approach
    • A first language model
    • Ambiguity for beginners
    • Summary
    • Exercises
  • Historical roots of Data-Intensive Linguistics
    • Why provide a history?
    • The history
      • Earliest times
      • Panini
      • Public availability of text
      • The Rosetta stone
      • Käding
    • Motivations for the scientfic study of communication
      • Telegraphy and Telephony
      • Information Theory
    • Summary
      • Key ideas
      • Key applications
    • Questions
• Finding Information in Text
  • Tools for finding and displaying text
    • Search tools for data-intensive linguistics
    • Using the UNIX tools
    • Sorting and counting text tokens
    • Lemmatization
    • Making n-grams
    • Filtering: grep
    • Selecting fields
      • AWK commands
      • AWK as a programming language
    • PERL programs
    • Summary
    • A final exercise.
    • A compendium of UNIX tools
      • Text processing
      • Data analysis
  • Concordances and Collocations
    • Concordances
    • Keyword-in-Context index
    • Collocations
    • Stuttgart corpus tools
      • Getting started
      • Queries
      • Manipulating the results
      • Other useful things
• Collecting and Annotating Corpora
  • Corpus design
    • Introduction
    • Choices in corpus design/collection
      • Reference Corpus or Monitor Corpus?
      • Where to get the data?
      • Copyright and legal matters
      • Choosing your own corpus
      • Size
      • Generating your own corpus
        • Planning
        • Subject population and Treatment
        • Language
        • Extraneous factors
        • Human factors in annotation
        • Be conservative (small c)
      • Which annotation scheme?
        • The semantics of annotations
        • External format of annotations
    • An annotated list of corpora
      • Speech corpora
  • SGML for Computational Linguists
  • Annotation Tools
• Statistics for Data-Intensive Linguistics
  • Probability and Language Models
    • Events and probabilities
      • Events:
      • Random variables:
      • Probabilities:
      • Conditional probabilities and independence:
      • Bayes rule
      • Medical diagnosis:
    • Statistical models of language
    • Case study: Language Identification
      • Unique strings
      • Common words
      • Markov models
      • Bayesian Decision Rules
        • Choice of priors may not matter:
    • Estimating Model Parameters
      • Results
    • Summary
    • Applying probabilities to Data-Intensive Linguistics
      • Contingency Tables
      • Text preparation
      • Contingency tables
      • Counting words in documents
      • Introduction
      • Bigram probabilities
      • ${\chi}^2$
      • Words and documents
  • Probability and information
    • Introduction
    • Data-intensive grocery selection
    • Entropy
    • Cross entropy
      • Why is the cross-entropy always more than the entropy?
    • Summary and self-check
    • Questions:
  • Hidden Markov Models and Part of Speech-Tagging
    • Graphical presentations of HMMs
    • Example
    • Transcript
• Applications of Data-Intensive Linguistics
  • Statistical Parsing
    • Introduction
    • The need for structure
    • Why statistical parsing?
    • The components of a parser
    • The standard probabilistic parser
    • Varieties of probabilistic parser
      • Exhaustive search
      • Beam search
      • Left incremental parsers
      • Alternative figures of merit
        • Probabilistic LR Parsing
        • Left-Corner Language Models
        • Data-Oriented Parsing
        • Word Statistics
      • Lexicalized grammars
        • Categorial Grammars
        • Tree-adjoining Grammars
        • Dependency-based Grammars
        • Link Grammar
      • Parsing as statistical pattern recognition
        • Lexical Dependency Parsing
        • Decision-tree parsing
        • Maximum entropy parsing
        • Oracle-based parsing
    • Conventional techniques for shallow parsing
    • Summary
    • Exercises
• References