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I intend to create a library for text simplification, and potentially would like to integrate your package.
The selection of a tokenizer has an impact on the obtained readability scores and I was wondering how you approached this issue.
Was there any specific reason for choosing the Tweet-Tokenizer over e.g. the default/recommended Nltk-Tokenizer which better depicts the Penn Treebank's definition of word-boundaries?
Thanks for your work on this nice project.
I intend to create a library for text simplification, and potentially would like to integrate your package.
The selection of a tokenizer has an impact on the obtained readability scores and I was wondering how you approached this issue.
Was there any specific reason for choosing the Tweet-Tokenizer over e.g. the default/recommended Nltk-Tokenizer which better depicts the Penn Treebank's definition of word-boundaries?
py-readability-metrics/readability/text/analyzer.py
Line 128 in 3ffb97f
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