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Towards a Theoretical Framework for Analyzing Complex Linguistic Networks 1st ed. 2016 [Kõva köide]

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  • Formaat: Hardback, 343 pages, kõrgus x laius: 235x155 mm, kaal: 6742 g, 25 Illustrations, color; 52 Illustrations, black and white; XXII, 343 p. 77 illus., 25 illus. in color., 1 Hardback
  • Sari: Understanding Complex Systems
  • Ilmumisaeg: 21-Jul-2015
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662472376
  • ISBN-13: 9783662472378
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Towards a Theoretical Framework for Analyzing Complex Linguistic Networks 1st ed. 2016Suurem pilt
  • Formaat: Hardback, 343 pages, kõrgus x laius: 235x155 mm, kaal: 6742 g, 25 Illustrations, color; 52 Illustrations, black and white; XXII, 343 p. 77 illus., 25 illus. in color., 1 Hardback
  • Sari: Understanding Complex Systems
  • Ilmumisaeg: 21-Jul-2015
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662472376
  • ISBN-13: 9783662472378
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783662472378.html
Märksõnad:

The aim of this book is to advocate and promote network models of linguistic systems that are both based on thorough mathematical models and substantiated in terms of linguistics. In this way, the book contributes first steps towards establishing a statistical network theory as a theoretical basis of linguistic network analysis the boarder of the natural sciences and the humanities. This book addresses researchers who want to get familiar with theoretical developments, computational models and their empirical evaluation in the field of complex linguistic networks. It is intended to all those who are interested in statistical models of linguistic systems from the point of view of network research. This includes all relevant areas of linguistics ranging from phonological, morphological and lexical networks on the one hand and syntactic, semantic and pragmatic networks on the other. In this sense, the volume concerns readers from many disciplines such as physics, linguistics, computer science and information science. It may also be of interest for the upcoming area of systems biology with which the chapters collected here share the view on systems from the point of view of network analysis.

Introduction v
Alexander Mehler
Andy Lucking
Sven Banisch
Philippe Blanchard
Barbara Frank-Job
1 On the Content of This Book v
2 Overview of the Book vii
2.1 Part I: Cognition vii
2.2 Part II: Topology vii
2.3 Part III: Syntax viii
2.4 Part IV: Dynamics viii
2.5 Part V: Resources ix
Part I Cognition
Language Networks as Models of Cognition: Understanding Cognition through Language
3(26)
Nicole M. Beckage
Eliana Colunga
1 Introduction
3(2)
2 Language as a Network
5(2)
2.1 Semantic Networks
5(1)
2.2 Phonological Networks
6(1)
3 Global Level Network Structure
7(4)
3.1 Small-World Structure
8(2)
3.2 Scale-Free Networks
10(1)
4 Human Performance in Relation to Network Structure
11(5)
4.1 Spreading Activation
11(4)
4.2 Frequency Effects
15(1)
5 Network Models within Linguistic Networks
16(7)
5.1 Acquisition
17(4)
5.2 Network Navigation
21(2)
6 Understanding Atypical Processes
23(2)
7 The Future of Language Networks
25(4)
References
26(3)
Path-Length and the Misperception of Speech: Insights from Network Science and Psycholinguistics
29(18)
Michael S. Vitevitch
Rutherford Goldstein
Elizabeth Johnson
1 Introduction
29(2)
2 Network Analysis: What Can Be Perceived When Speech Is Misperceived?
31(3)
3 Psycholinguistic Experiment: What Is Perceived When Speech Is Misperceived?
34(6)
3.1 Method
35(2)
3.2 Results
37(3)
4 Conclusion
40(7)
References
43(4)
Structure and Organization of the Mental Lexicon: A Network Approach Derived from Syntactic Dependency Relations and Word Associations
47(36)
Simon De Deyne
Steven Verheyen
Gert Storms
1 Introduction
47(6)
1.1 Macro-, Meso-, and Microscopic Properties of the Mental Lexicon
48(2)
1.2 Acquiring a Mental Lexicon through Language
50(1)
1.3
Chapter Outline
51(2)
2 Constructing the Networks
53(3)
2.1 Mental Networks
53(1)
2.2 Language Networks
54(2)
3 Exploring the Structure of Language and Mental Networks
56(14)
3.1 Macroscopic Structure
56(3)
3.2 Mesoscopic Structure
59(7)
3.3 Semantic Relatedness Evaluation
66(4)
4 Discussion
70(13)
4.1 Relationship between Language and Word Associations
72(1)
4.2 Final Words
73(1)
References
74(9)
Part II Topology
Network Motifs Are a Powerful Tool for Semantic Distinction
83(24)
Chris Biemann
Lachezar Krumov
Stefanie Roos
Karsten Weihe
1 Introduction
84(2)
2 Related Work
86(1)
3 The Case Studies
87(16)
3.1 Co-occurrence Graphs from Natural Vs. Artificial Language
87(7)
3.2 Co-occurrence Graphs from Verbs Vs. Other Word Classes
94(5)
3.3 Peer-to-Peer Streaming Networks
99(1)
3.4 Co-Authorship Networks from Two Subdisciplines of Physics
100(2)
3.5 Mailing Networks
102(1)
4 Conclusions and Outlook
103(4)
References
103(4)
Multidimensional Analysis of Linguistic Networks
107(26)
Tanya Araujo
Sven Banisch
1 Introduction
107(2)
2 Linguistic Networks Are Special
109(5)
2.1 Three Types of Networks
109(3)
2.2 Network Induction
112(2)
3 Three Levels of Statistical Analysis
114(6)
3.1 A Brief Note on Signal Processing on Graphs
115(1)
3.2 The Statistical Levels
115(1)
3.3 Stylized Facts in Network Analysis
116(2)
3.4 Levels in the Statistical Analysis of Networks
118(2)
4 On the Intelligibility of Statistical Indicators in Linguistic Networks
120(4)
4.1 Path-Based Measures
120(1)
4.2 Links and Flows, Structure and Function
121(1)
4.3 Types of Network Flow
122(1)
4.4 Flow in Linguistic Networks
122(2)
5 Examples
124(1)
6 Discussion
124(2)
7 Concluding Remarks
126(7)
References
127(6)
Semantic Space as a Metapopulation System: Modelling the Wikipedia Information Flow Network
133(20)
A. Paolo Masucci
Alkiviadis Kalampokis
Victor M. Eguiluz
Emilio Hernandez-Garcia
1 Introduction
133(3)
2 The Dataset
136(1)
3 Topology of the Semantic Space
136(5)
4 Modelling the Semantic Space
141(2)
5 Discussion
143(10)
Appendix
145(3)
References
148(5)
Are Word-Adjacency Networks Networks?
153(14)
Katharina Anna Zweig
1 Introduction
153(3)
1.1 Perspectives of Network Analysis
154(2)
2 Definitions
156(1)
2.1 Definition of Word-Adjacency Networks
156(1)
3 Walk-Based Methods and Network Flows
157(3)
3.1 Models of Walks
159(1)
4 Word-Adjacency Networks in the Literature
160(2)
5 Summary
162(5)
References
163(4)
Part III Syntax
Syntactic Complex Networks and Their Applications
167(20)
Radek Cech
Jan Macutek
Haitao Liu
1 Introduction
167(1)
2 Basic Characteristics of Syntactic Networks
168(1)
3 Early Development of Syntactic Complex Network Analysis
169(3)
4 Role of Syntax in Syntactic Dependency Complex Networks
172(5)
5 Preprocessing of Data for a Syntactic Complex Network Analysis -- Pitfalls to be Avoided
177(2)
6 Applications of Syntactic Complex Networks to Language Typology and Acquisition
179(3)
6.1 Language Typology
180(1)
6.2 Language Acquisition
181(1)
7 Conclusion
182(5)
References
183(4)
Function Nodes in Chinese Syntactic Networks
187(16)
Xinying Chen
Haitao Liu
1 Introduction
187(2)
2 The Chinese Dependency Networks for This Study
189(3)
3 Chinese Function Words
192(1)
4 Chinese Function Words in the Language Networks
193(5)
4.1 Network Properties of Chinese Function Words
193(3)
4.2 Network Manipulation
196(2)
5 Conclusion
198(5)
References
199(4)
Non-crossing Dependencies: Least Effort, Not Grammar
203(34)
Ramon Ferrer-i-Cancho
1 Introduction
203(4)
2 The Syntactic Dependency Structure of Sentences
207(1)
3 The Null Hypothesis
208(4)
4 Alternative Hypotheses
212(7)
4.1 A Principle of Minimization of Dependency Crossings
213(1)
4.2 A Principle of Minimization of Dependency Lengths
214(2)
4.3 The Relationship between Minimization of Crossings and Minimization of Dependency Lengths
216(3)
5 A Stronger Null Hypothesis
219(5)
5.1 The Probability That Two Edges Cross
220(1)
5.2 The Expected Number of Edge Crossings
221(3)
6 Another Stronger Null Hypothesis
224(1)
7 Predictions, Testing and Selection
224(3)
8 Discussion
227(10)
Appendix
229(2)
References
231(6)
Part IV Dynamics
Simulating the Effects of Cross-Generational Cultural Transmission on Language Change
237(20)
Tao Gong
Lan Shuai
1 Introduction
237(3)
2 Modified Acquisition Framework
240(2)
3 Simulation Results
242(2)
4 Discussions and Conclusions
244(13)
Appendix
248(6)
References
254(3)
Social Networks and Beyond in Language Change
257(22)
Gareth J. Baxter
1 Introduction
257(1)
2 Utterance Selection Model of Language Change
258(2)
3 Numerical Model
260(4)
4 Analysis
264(3)
5 Social Networks in the Neutral Model
267(1)
6 Weighted Interactor Selection
268(6)
6.1 Asymmetry Independent of Network Structure
269(3)
6.2 Asymmetry Depends on Speakers Degree
272(2)
7 Conclusions
274(5)
Appendix
275(1)
References
276(3)
Emergence of Dominant Opinions in Presence of Rigid Individuals
279(20)
Suman Kalyan Maity
Animesh Mukherjee
1 Introduction
279(3)
2 Related Work
282(1)
3 The Model Description
283(1)
4 Results and Discussion
283(7)
4.1 The Mean-Field Case
283(4)
4.2 Scale-Free Networks
287(3)
5 Time-Varying Networks
290(4)
5.1 Dataset Description
291(1)
5.2 The Model Adaptation in the Time-Varying Setting
291(1)
5.3 Results and Discussion
292(2)
6 Conclusions and Future Works
294(5)
References
294(5)
Part V Resources
Considerations for a Linguistic Network Markup Language
299(32)
Maik Stuhrenberg
Nils Diewald
Rudiger Gleim
1 Introduction
299(1)
2 Data Formats
299(5)
2.1 Data Models
300(1)
2.2 Data Structures
301(1)
2.3 Data Serialization
302(2)
3 Existing Formats
304(11)
3.1 GML
305(1)
3.2 XGMML
306(1)
3.3 GraphXML
307(2)
3.4 GraphML
309(2)
3.5 GXL
311(2)
3.6 GrAF
313(2)
3.7 Summary
315(1)
4 Network Tools
315(3)
5 Proposal for a Linguistic Network Markup Language
318(9)
5.1 Extending GraphML by Redefinition
320(2)
5.2 Extending GraphML by XML Namespaces
322(3)
5.3 Example Instance
325(2)
6 Conclusion
327(4)
References
327(4)
Linguistic Networks -- An Online Platform for Deriving Collocation Networks from Natural Language Texts
331
Alexander Mehler
Rudiger Gleim
1 Introduction
331(3)
2 On the Parameter Space of LN
334(2)
3 The Software Architecture of LN
336(4)
4 Summary
340
References
340