Detect quoted text in emails I have small dataset (<10k) of emails (plaintext) that need to be classified. Currently I'm doing research on topic of email preprocessing and I can't find any suitable solution for quoted text detection. There's plenty of answered similar question on StackOverflow (example question), but they all agree on rule-based solutions (regex). But I have rather diverse dataset with no specific delimeter between main and quoted parts. So I want to know, is there any other solution/research apart from regex?

How many possible delimiters could there be? I guess that you can detect the vast majority of them with a rule-based system. You should build a rule-based system at least as a benchmark. Such a system would be easy to build, so you would have something that works pretty fast. While building it, you would have a chance to do an exploratory analysis of your data, which would be useful if you decide to build something based on machine learning next (what preprocessing is needed, what are the problems with the data, some strange edge cases). Once you have it, you can start working on a machine learning solution. Given the benchmark, you would know how good or bad the performance of the model is and if the performance improvements are worth the time spend on it. The reason you see so many answers suggesting rule-based solutions is that in most cases they are enough for the problem.
Moreover, if you have a dataset with less than 10k emails, it probably would not be enough to train a machine learning model that would be more sophisticated and much better performant than the rule-based system. Especially, it won't be able to learn the unorthodox delimiters, because for this you would need many examples per each such delimiter. So if you want to use machine learning, you probably would need to gather more data first.
