My Book: Natural Language Processing for Corpus Linguistics
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.
Interactive Labs: https://doi.org/10.24433/CO.3402613.v1
My Free MOOCs (edX)
Text Analytics 1: Introducing Natural Language Processing: Learn the core techniques of computational linguistics alongside the cognitive science that makes it all possible and the ethics we need to use it properly.
Text Analytics 2: Visualizing Natural Language Processing in Python: Extend your knowledge of the core techniques of computational linguistics by working through case-studies and visualizing their results.
My Python Packages
And here’s the Python package I made to introduce students to NLP:
Here are some code notebooks for exercises in computational linguistics:
Finally, here are some interactive case-studies from my book Natural Language Processing for Corpus Linguistics: https://doi.org/10.24433/CO.3402613.v1