This recipe will show how to generate basic concordances of a word and showing it within a textual context. We will use "find and replace" strategy known as regular expressions in this approach. Regex, as it is also known is universal to most programming languages and is a well documented method of parsing text. This recipe is based on Jinman's cookbook.
This recipe uses Python and WordNet to explore meaning in a text.
This recipe shows how to create a basic concordance tool in Python.
This recipe uses Voyant Tools to explore vocabulary change over time in a corpus of 7 Canadian Throne Speeches (from 2006 to 2013).
This is a recipe to build a simple concordance from a text.
This recipe takes a French language text and adds it to the TAPoR workspace for textual analysis. This recipe ensures that the fundamental task of loading text into a text analysis environment is accomplished correctly. For proper analysis, the text must be interpreted by the computer in the same way in which you enter it, including accented characters. There are a variety of ways in which text can be encoded by operating systems and applications during text entry and storage.
This is a recipe for using Concordance tools to explore a Plain text corpus for topics or key words of interest, and generate a list of terms in Context for later analysis.
This recipe takes a text with known syntactic dependencies and explores those using tools such as Word List, Concordance, Co-occurrence and Collocation.
This recipe uses an Aggregate Text tool, frequency lists, Concordance and Collocation tools to explore how a writer’s use of language changes over a lifetime.
This recipe takes a text and explores its use of theory by using tools such as Word list, Concordance, and Collocation.