Intro
Corpora and the Changing Society
Editorial page
Title page
Copyright page
Table of contents
Acknowledgements
Introduction: Corpora and the changing society
Part I. Changing society
The great temptation: What diachronic corpora do and do not reveal about social change
1. Introduction
2. Five pitfalls in the analysis of diachronic corpus data
2.1 Corpus frequencies (semasiological frequencies) are not always equivalent to frequencies of entities and events in the real world (onomasiological frequencies) 2.2 Corpus frequencies of polysemous words need to be broken down into sense-specific and construction-specific frequencies
2.3 Correlations in large datasets may be spurious
2.4 Comparisons of frequency trends in diachronic corpora require adequate statistical treatment
2.5 It is not always easy to disentangle social change and linguistic change
3. Giving in to temptation: A case study of the English make-causative
3.1 The English make-causative construction
3.2 Corpus data and descriptive statistics 3.3 Using distributional semantics to study the development of the make-causative
3.4 Discussion
4. Conclusions
References
Corpora
Other references
Changes in society and language: Charting poverty
1. Introduction
2. Data and pre-processing
2.1 The EEBO Collection as sampler corpus
2.2 The CLMET3.0 corpus
2.3 The pre-processing step of spelling normalization
3. Methods
3.1 Data-based and data-driven approaches
3.2 Document classification
3.3 Topic modelling
3.4 Conceptual maps
4. Results and discussion
4.1 Dictionary-based approach 4.2 Topic modelling
4.3 Conceptual maps
5. Conclusions
References
Corpora and software
Other references
Finding evidence for a changing society: A collocational study of medical discourse in 1500-1800
1. Introduction
2. Background
3. Materials and method
4. Results
4.1 The Corpus of Early Modern English Medical Texts (1500-1700)
4.2 The Corpus of Late Modern English Medical Texts (1700-1800)
5. Discussion
6. Conclusion
References
Corpora and software
Other references Semantic neology: Challenges in matching corpus-based semantic change to real-world change
1. Introduction
2. Data and methods
2.1 Data and tools
2.2 Tracking the neosemes
3. Case studies
3.1 Case study 1: Birther
3.2 Case study 2: Normalisation
3.3 Case study 3: Cougar
3.4 Case study 4: Snowflake
3.5 Case study 5: Ghosting
4. Discussion
4.1 Challenges
4.2 Measures shown to allow or enhance system performance
4.3 Sociolinguistic insights gained in the study
5. Conclusion
References
Corpora and tools
Other references.