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Preface; Acknowledgments; Contents; Contributors; Introduction to the Volume; 1 Why This Volume?; 2 Contributions; Statistical Laws in Linguistics; 1 Introduction; 2 Examples and Observations; 3 Interpretation of Linguistic Laws; 4 Statistical Analysis; 4.1 Graphical Approaches; 4.2 Likelihood Methods; 4.3 Critical Discussion; 5 Relation Between Laws; 6 Discussion; References; Complexity and Universality in the Long-Range Order of Words; 1 Introduction; 2 Universality in the Entropy of Word Ordering; 3 An Entropy Measure of Semantic Information
3.1 Information in the Distribution of Individual Words3.2 Extraction of Semantic Networks; 4 Conclusions; References; Symmetry and Universality in Language Change; 1 Three Notions of Universality in Language Change; 2 Asymmetry in Language Change: The Universal S-Curve; 3 Language Change with No Asymmetry; 4 Asymmetry in Interaction Frequencies; 5 Asymmetry in Social Attitudes; 6 Asymmetry in the Variants; 7 Asymmetry in Variant Frequencies; 8 Discussion; References; Dynamics on Expanding Spaces: Modeling the Emergence of Novelties; 1 Introduction; 2 Simon-Like Models
2.1 Plain Simon's Model2.2 Simon's Model with Time Dependent Sublinear Invention Probability; 2.3 Simon's Model with Memory; 3 The Sample-Space Reducing Model; 4 Hoppe Urn Model; 5 Urn Model with Triggering; 5.1 Model Definition; 5.2 Computation of the Asymptotic Heaps' and Zipf's Laws; 6 Conclusions; References; Generating Non-plagiaristic Markov Sequences with Max Order Sampling; 1 Introduction; 2 A Simple Example; 3 Building the No-Good Automaton; 4 Sampling Sequences with a Maximum Order Guarantee; 4.1 Background on Belief Propagation; 4.2 A Factor Graph Model of Max Order Sequences
4.3 Sampling Max Order Sequences5 Evaluation; 5.1 Solution Loss; 5.2 Sampling; 5.3 Example; 6 Conclusion; References; Integrating Purpose and Revision into a Computational Model of Literary Generation; 1 Introduction; 2 Computational and Cognitive Models of the Writing Task; 2.1 Poetry Generation; 2.2 Narrative Generation; 2.3 Cognitive Accounts of Writing; 3 Story Construction Based on Purpose-Driven Revision; 4 Systems that Follow the ICTIVS Model; 4.1 WASP; 4.2 STellA; 4.3 A System for Composition of Narrative Discourse; 5 Discussion; 6 Conclusions; References
Detection of Computer-Generated Papers in Scientific Literature1 Introduction; 2 Texts Generation; 2.1 Markov Chain; 2.2 Handwritten Probabilistic Context-Free Grammar; 3 Lexical and Stylistics Indices; 3.1 Vocabulary Richness; 3.2 Length and Structure of Sentences; 3.3 Distribution of Word-Type Frequencies; 4 Distance and Hierarchical Clustering; 5 ROUGE Measures; 6 Conclusion; References; Universality of Stylistic Traits in Texts; 1 Introduction; 2 Stylometry; 3 Universality in Authorship Attribution; 4 Experiments; 4.1 Corpus; 4.2 Experimental Settings; 4.3 Results; 5 Conclusion
3.1 Information in the Distribution of Individual Words3.2 Extraction of Semantic Networks; 4 Conclusions; References; Symmetry and Universality in Language Change; 1 Three Notions of Universality in Language Change; 2 Asymmetry in Language Change: The Universal S-Curve; 3 Language Change with No Asymmetry; 4 Asymmetry in Interaction Frequencies; 5 Asymmetry in Social Attitudes; 6 Asymmetry in the Variants; 7 Asymmetry in Variant Frequencies; 8 Discussion; References; Dynamics on Expanding Spaces: Modeling the Emergence of Novelties; 1 Introduction; 2 Simon-Like Models
2.1 Plain Simon's Model2.2 Simon's Model with Time Dependent Sublinear Invention Probability; 2.3 Simon's Model with Memory; 3 The Sample-Space Reducing Model; 4 Hoppe Urn Model; 5 Urn Model with Triggering; 5.1 Model Definition; 5.2 Computation of the Asymptotic Heaps' and Zipf's Laws; 6 Conclusions; References; Generating Non-plagiaristic Markov Sequences with Max Order Sampling; 1 Introduction; 2 A Simple Example; 3 Building the No-Good Automaton; 4 Sampling Sequences with a Maximum Order Guarantee; 4.1 Background on Belief Propagation; 4.2 A Factor Graph Model of Max Order Sequences
4.3 Sampling Max Order Sequences5 Evaluation; 5.1 Solution Loss; 5.2 Sampling; 5.3 Example; 6 Conclusion; References; Integrating Purpose and Revision into a Computational Model of Literary Generation; 1 Introduction; 2 Computational and Cognitive Models of the Writing Task; 2.1 Poetry Generation; 2.2 Narrative Generation; 2.3 Cognitive Accounts of Writing; 3 Story Construction Based on Purpose-Driven Revision; 4 Systems that Follow the ICTIVS Model; 4.1 WASP; 4.2 STellA; 4.3 A System for Composition of Narrative Discourse; 5 Discussion; 6 Conclusions; References
Detection of Computer-Generated Papers in Scientific Literature1 Introduction; 2 Texts Generation; 2.1 Markov Chain; 2.2 Handwritten Probabilistic Context-Free Grammar; 3 Lexical and Stylistics Indices; 3.1 Vocabulary Richness; 3.2 Length and Structure of Sentences; 3.3 Distribution of Word-Type Frequencies; 4 Distance and Hierarchical Clustering; 5 ROUGE Measures; 6 Conclusion; References; Universality of Stylistic Traits in Texts; 1 Introduction; 2 Stylometry; 3 Universality in Authorship Attribution; 4 Experiments; 4.1 Corpus; 4.2 Experimental Settings; 4.3 Results; 5 Conclusion