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[PlanetHopf]

On the subject of keywords, specific mathematical measures from the field of corpus linguistics are readily available, which do not require us to anticipate the full context or subjective interpretations of a text. In corpus linguistics, one fundamental approach to identifying keywords is through frequency analysis. This involves counting the number of times each word appears in a given text or corpus.

However, frequency alone can be misleading, as common words like 'the' and 'is' often appear frequently but carry little specific meaning. To refine this, techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) are employed. TF-IDF weighs the frequency of a word against its commonness across multiple texts. Words that are frequent in a specific text but rare in general are highlighted as significant or keywords.

Another approach is collocation analysis, which examines the frequency with which words co-occur in close proximity. This method helps in understanding the contextual usage of words, revealing patterns that might not be apparent through simple frequency analysis.

Additionally, concordance analysis provides insights into how words are used in context. It involves examining every occurrence of a specific word or phrase within a text to see the words immediately surrounding it. This can reveal patterns in the usage and meaning that are not evident from frequency counts alone.

Lastly, dispersion plots can be useful. They show the distribution of words across a text, revealing patterns in the usage over the course of the text.

These methods, grounded in quantitative analysis, offer objective measures to identify and understand keywords in a text, bypassing the need for subjective interpretation or context anticipation. This aligns with the goal in complexity science of finding comprehensive, interdisciplinary frameworks that can be mathematically defined and understood.

[PlanetHopf]

I have been thinking of using n-ordered hypergraphs with arbitrary depth for our mathematical framework, what do you think?

[IChapman10]

I think n-ordered hypergraphs with arbitrary depth sounds good to me. If I am understanding correctly, this should provide adequate tracing and groupings. Right?