Jonathan Dunn – författare

Corpus analysis can be expanded and scaled up by incorporating computational methods from natural language processing. This Element shows how text classification and text similarity models can extend our ability to undertake corpus linguistics across very large corpora. These computational methods are becoming increasingly important as corpora grow too large for more traditional types of linguistic analysis. We draw on five case studies to show how and why to use computational methods, ranging from usage-based grammar to authorship analysis to using social media for corpus-based sociolinguistics. Each section is accompanied by an interactive code notebook that shows how to implement the analysis in Python. A stand-alone Python package is also available to help readers use these methods with their own data. Because large-scale analysis introduces new ethical problems, this Element pairs each new methodology with a discussion of potential ethical implications.

Corpus analysis can be expanded and scaled up by incorporating computational methods from natural language processing. This Element shows how text classification and text similarity models can extend our ability to undertake corpus linguistics across very large corpora. These computational methods are becoming increasingly important as corpora grow too large for more traditional types of linguistic analysis. We draw on five case studies to show how and why to use computational methods, ranging from usage-based grammar to authorship analysis to using social media for corpus-based sociolinguistics. Each section is accompanied by an interactive code notebook that shows how to implement the analysis in Python. A stand-alone Python package is also available to help readers use these methods with their own data. Because large-scale analysis introduces new ethical problems, this Element pairs each new methodology with a discussion of potential ethical implications.

Corpus analysis can be expanded and scaled up by incorporating computational methods from natural language processing. This Element shows how text classification and text similarity models can extend our ability to undertake corpus linguistics across very large corpora. These computational methods are becoming increasingly important as corpora grow too large for more traditional types of linguistic analysis. We draw on five case studies to show how and why to use computational methods, ranging from usage-based grammar to authorship analysis to using social media for corpus-based sociolinguistics. Each section is accompanied by an interactive code notebook that shows how to implement the analysis in Python. A stand-alone Python package is also available to help readers use these methods with their own data. Because large-scale analysis introduces new ethical problems, this Element pairs each new methodology with a discussion of potential ethical implications.

This Element introduces a usage-based computational approach to Construction Grammar that draws on techniques from natural language processing and unsupervised machine learning. This work explores how to represent constructions, how to learn constructions from a corpus, and how to arrange the constructions in a grammar as a network. From a theoretical perspective, this Element examines how construction grammars emerge from usage alone as complex systems, with slot-constraints learned at the same time that constructions are learned. From a practical perspective, this work is accompanied by a Python package which enables linguists to incorporate construction grammars into their own corpus-based work. The computational experiments in this Element are important for testing the learnability, variability, and confirmability of Construction Grammar as a theory of language. All code examples will leverage the cloud computing platform Code Ocean to guide readers through implementation of these algorithms.

This Element presents a computational theory of syntactic variation that brings together (i) models of individual differences across distinct speakers, (ii) models of dialectal differences across distinct populations, and (iii) models of register differences across distinct contexts. This computational theory is based in Construction Grammar (CxG) because its usage-based representations can capture differences in productivity across multiple levels of abstraction. Drawing on corpora representing over 300 local dialects across fourteen countries, this Element undertakes three data-driven case-studies to show how variation unfolds across the entire grammar. These case-studies are reproducible given supplementary material that accompanies the Element. Rather than focus on discrete variables in isolation, we view the grammar as a complex system. The essential advantage of this computational approach is scale: we can observe an entire grammar across many thousands of speakers representing dozens of local populations.

This Element introduces a usage-based computational approach to Construction Grammar that draws on techniques from natural language processing and unsupervised machine learning. This work explores how to represent constructions, how to learn constructions from a corpus, and how to arrange the constructions in a grammar as a network. From a theoretical perspective, this Element examines how construction grammars emerge from usage alone as complex systems, with slot-constraints learned at the same time that constructions are learned. From a practical perspective, this work is accompanied by a Python package which enables linguists to incorporate construction grammars into their own corpus-based work. The computational experiments in this Element are important for testing the learnability, variability, and confirmability of Construction Grammar as a theory of language. All code examples will leverage the cloud computing platform Code Ocean to guide readers through implementation of these algorithms.

This Element presents a computational theory of syntactic variation that brings together (i) models of individual differences across distinct speakers, (ii) models of dialectal differences across distinct populations, and (iii) models of register differences across distinct contexts. This computational theory is based in Construction Grammar (CxG) because its usage-based representations can capture differences in productivity across multiple levels of abstraction. Drawing on corpora representing over 300 local dialects across fourteen countries, this Element undertakes three data-driven case-studies to show how variation unfolds across the entire grammar. These case-studies are reproducible given supplementary material that accompanies the Element. Rather than focus on discrete variables in isolation, we view the grammar as a complex system. The essential advantage of this computational approach is scale: we can observe an entire grammar across many thousands of speakers representing dozens of local populations.

The first section focuses on efforts by people of multiple faiths to live together within their contexts, including such efforts within a neighborhood in urban Manchester;

The first section focuses on efforts by people of multiple faiths to live together within their contexts, including such efforts within a neighborhood in urban Manchester;