Hi David,

You can look into the ROUGE package which does n-gram based matches.
https://huggingface.co/spaces/evaluate-metric/rouge

You will find actual n-gram computation code here https://github.com/google-research/google-research/blob/master/rouge/rouge_scorer.py

Also, Fuzzy string matching is another efficient approach to do substring matching https://github.com/seatgeek/thefuzz

Thanks,
Mousumi


On Fri, Jun 23, 2023 at 9:39 AM David Beauchamp via Corpora <corpora@list.elra.info> wrote:
Hi all, I'm doing analysis on a corpus on tweets from institutions.  Regarding analysis of n-grams, it is quite unusual in that there are many repeated exact tweets, or very similar tweets, leading to long super strings of often 9 or 10 or more words together.  Naturally this makes accurate counting and classifying difficult due to the overlapping substrings.  Does anyone know of any approaches or software which can count and classify n-grams in such circumstances?  I am aware of approaches outlined by Buerki (2017) and O'Donnell (2011), but these do not seem practical due to the excessive length of the n-grams in the corpus.  Does anyone know of any accessible methods or packages?

Any input much appreciated.
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