Daniel Keller – författare

This Element offers intermediate or experienced programmers algorithms for Corpus Linguistic (CL) programming in the Python language using dataframes that provide a fast, efficient, intuitive set of methods for working with large, complex datasets such as corpora. This Element demonstrates principles of dataframe programming applied to CL analyses, as well as complete algorithms for creating concordances; producing lists of collocates, keywords, and lexical bundles; and performing key feature analysis. An additional algorithm for creating dataframe corpora is presented including methods for tokenizing, part-of-speech tagging, and lemmatizing using spaCy. This Element provides a set of core skills that can be applied to a range of CL research questions, as well as to original analyses not possible with existing corpus software.

This Element offers intermediate or experienced programmers algorithms for Corpus Linguistic (CL) programming in the Python language using dataframes that provide a fast, efficient, intuitive set of methods for working with large, complex datasets such as corpora. This Element demonstrates principles of dataframe programming applied to CL analyses, as well as complete algorithms for creating concordances; producing lists of collocates, keywords, and lexical bundles; and performing key feature analysis. An additional algorithm for creating dataframe corpora is presented including methods for tokenizing, part-of-speech tagging, and lemmatizing using spaCy. This Element provides a set of core skills that can be applied to a range of CL research questions, as well as to original analyses not possible with existing corpus software.