CoreNLP VS Mallet

A suitable LLM library such as Hugging Face's Transformers or Stanford's CoreNLP

The Stanford CoreNLP Model

If your looking at spacy have a look at Apache OpenNLP and Core NLP.

Tokenize with NLTK, SpaCy or CoreNLP

It should not take nearly that long. However, again I must recommend you take this conversation to github

Stanford has a great program and the Stanford NLP Group maintains CoreNLP which I have used before.

So why not use the Stanford CoreNLP library?

If you need NER, there’s no need to implement it yourself. There are several popular libraries that can do this for you nowadays. Five of these libraries, Stanford CoreNLP, NLTK, OpenNLP, SpaCy, and GATE, were already mentioned in the title.

Check something like this out to start: https://stanfordnlp.github.io/CoreNLP/

You don't actually even need to learn how to do deep learning if you're doing something fairly basic, which it sounds like you are. There are a lot of good tools you can use basically straight out of the box for something like this. Check out https://huggingface.co/course/chapter1, https://course.spacy.io/en/, https://guide.allennlp.org/ and https://www.nltk.org/book/. If java's more your thing, add https://stanfordnlp.github.io/CoreNLP/ to the list.

Nevertheless, you might take a look at the practice of "topic modeling" and get ready for a whole lot of abstruse statistics. One place to start might be Ted Underwoods Topic Modeling Made Just Simple Enough. If you just want to play with some pre-written software that does this kind of thing, you might want to look at MALLET.