"Specialists in quantitative linguistics the world over have recourse to a solid and universal methodology. These days, their methods and mathematical models must also respond to new communication phenomena and the flood of data produced daily. While various disciplines (computer science, media science) have different ways of processing this onslaught of information, the linguistic approach is arguably the most relevant and effective. This book includes recent results from many renowned contemporary practitioners in the field. Our target audiences are academics, researchers, graduate students, and others involved in linguistics, digital humanities, and applied mathematics"--.

• LANGUAGE AND TEXT
• Editorial page
• Title page
• Copyright page
• Table of contents
• Introduction
• Part I. Theory and models
• On the impact of the initial phrase length on the position of enclitics in Old Czech
  • 1. Introduction
  • 2. Classification of the word ordering of enclitics in Old Czech
  • 3. Language material
  • 4. Methodology
  • 5. Results
  • 6. Conclusion
  • Funding
  • References
• Term distance, frequency and collocations
  • 1. Introduction
  • 2. Δ-score and Pointwise Mutual Information
  • 3. Data and technical method
  • 4. Collocations
    • 4.1 Frequency and context enlargement
    • 4.2 Distance
  • 5. Discussion
  • References
• A method for the comparison of general sequences via type-token ratio
  • 1. Introduction: Sequence analysis and its importance
  • 2. Method: Quantitative linguistics and the most basic methodology
    • Step 1: Normalization of the alphabet
    • Step 2: Length normalization
    • Step 3: Contextual data collection from n-grams
    • Step 4: Quantification of properties
    • Step 5: Interpretation, visualizations, and beyond
  • 3. Visualization methods
    • 3.1 Basic line-chart
    • 3.2 Classical Multidimensional Scaling (MDS)
    • 3.3 t-Distributed Stochastic Neighbor Embedding
  • 4. Models of sequence behavior in our method
    • 4.1 Model of truly random sequences
    • 4.2 Model of minimal TTR
    • 4.3 Model of maximal TTR
  • 5. Method artefacts and specific n intervals
    • 5.1 Interval of exhausting vocabulary Q
    • 5.2 Interval of vocabulary saturation C
    • 5.3 Interval of maximum variance H
  • 6. Comparison to other, similar purpose methods
  • 7. Conclusions
  • Funding
  • References
• Quantitative analysis of syllable properties in Croatian, Serbian, Russian, and Ukrainian
  • 1. Introduction
  • 2. Methodology and language material
  • 3. Results
    • 3.1 Syllable frequency
    • 3.2 Syllable length
    • 3.3 Relation between syllable frequency and syllable length
    • 3.4 Menzerath-Altmann law
  • 4. Conclusions
  • Funding
  • References
• N-grams of grammatical functions and their significant order in the Japanese clause
  • 1. Aim of the study
  • 2. Descriptions of data and grammatical definitions
  • 3. Hypotheses and methodology
  • 4. Results for grammatical function types in the first position and in the second or later position
  • 5. Results for grammatical function types in the position directly preceding the predicate and preceding it by two or more units
  • 6. Results for bi-grams of grammatical function types including the subject or the object
  • 7. Conclusions
  • Abbreviations
  • Funding
  • Software and digital dictionaries
  • References
• Linking the dependents: Quantitative-linguistic hypotheses on valency
  • 1. Introduction
  • 2. The functional equivalents of coding semantic case
  • 3. Quantitative definitions of valency
  • 4. Derivation of linguistic hypotheses of valency
  • 5. FrameNet data
  • 6. Statistical hypotheses and testing
  • 7. Interpretations and conclusions
  • References
• Grammar efficiency and the One-Meaning–One-Form Principle
  • 1. Introduction
  • 2. Measures of the degree of violation of the Principle
  • 3. Hengeveld’s part-of-speech systems
  • 4. The previous grammar-efficiency formula
  • 5. The new grammar-efficiency formula
  • 6. The efficiency of basic parts-of-speech system types
  • 7. Conclusion
  • References
• Distribution and characteristics of commonly used words across different texts in Japanese
  • 1. The law of distribution of words
  • 2. Previous studies
  • 3. Data and method
  • 4. Results
  • 5. Interpretation
  • 6. Conclusion and further challenges
  • Funding
  • References
• Part II. Empirical studies
• The perils of big data
  • 1. Motivation
  • 2. Some background
  • 3. The muddle in the middle
  • 4. Faith and reason
  • 5. Data, and more data
  • 6. In short
  • Acknowledgements
  • References
• From distinguishability to informativity: A quantitative text model for detecting random texts
  • 1. Introduction
  • 2. Text corpora and their quantification
    • 2.1 Quantification
    • 2.2 Text corpora and their randomization
    • 2.3 Classification and evaluation methods
  • 3. Results
  • 4. Discussion
  • 5. Conclusion
  • References
• A Modern Greek readability tool: Development of evaluation methods
  • 1. Introduction
  • 2. Readability analysis: A short literature review
  • 3. Methodology
    • 3.1 Corpus
    • 3.2 Features
    • 3.3 Machine learning algorithm: Random Forest
  • 4. Results
  • 5. Conclusion
  • Funding
  • References
• Phonological properties as predictors of text success
  • 1. Introduction
    • 1.1 Beauty-in-averageness effect
    • 1.2 Effect of euphony
    • 1.3 Spoken data vs. written corpora
  • 2. Data
  • 3. Method
    • 3.1 Observed units
    • 3.2 Success rate
    • 3.3 Resampling
  • 4. Results
  • 5. Discussion
  • Acknowledgement
  • References
• Calculating the victory chances: A stylometric insight into the 2018 Czech presidential election
  • 1. Introduction
  • 2. Material and methods
  • 3. Results and interpretations
    • 3.1 The new-year addresses: A stylometric perspective
    • 3.2 Marek Hilšer vs. Michal Horáček: The keyword analysis
  • 4. Conclusions
  • References
• Topological mapping for visualisation of high-dimensional historical linguistic data
  • 1. Introduction
  • 2. Nonlinearity
    • 2.1 Nonlinearity in natural processes
    • 2.2 Nonlinearity in data
    • 2.3 Nonlinearity in linguistic data
  • 3. The problem
  • 4. Topological mapping
    • 4.1 Topology
    • 4.2 Projection of topological structure into low-dimensional space
    • 4.3 Preservation of nonlinearity
    • 4.4 Example
  • 5. Conclusion
  • References
• Book genre and author’s gender recognition based on titles: The example of the bibliographic corpus of microtexts
  • 1. The problem
  • 2. Data and research hypotheses
  • 3. Methodology
  • 4. Experiments and results
    • 4.1 Recognizing the literary genre of the text
    • 4.2 Automatic recognition of the author’s gender
  • 5. Conclusions
  • Funding
  • References
• Quantitative analysis of bibliographic corpora: Statistical features, semantic profiles, word spectra
  • 1. Large-scale bibliographies as text corpora
  • 2. Data and hypotheses
  • 3. Research method
  • 4. Results: An overview
  • 5. Results: Statistical distributions
  • 6. Conclusions
  • Funding
  • Sources
  • References
• Analysis of English text genre classification based on dependency types
  • 1. Introduction
  • 2. Treebank establishment
  • 3. Methods
    • 3.1 Principal component analysis
    • 3.2 Hierarchical cluster analysis
    • 3.3 Random Forest
  • 4. Results and discussion
    • 4.1 PCA
    • 4.2 Text clustering
    • 4.3 Random forest
  • 5. Conclusions
  • Acknowledgements
  • References
  • Appendix A. English universal relations
  • Appendix B. The proportion of dependency type across genres
• Index