Earlier this year I published a paper [1] that was partly about recognizing place references from text. Various NLP libraries and gpt-3.5-turbo were used in the comparison. The comparison was not the focus of the paper and newer LLMs are probably better, but in the specific case, gpt had a lower precision score than most of the tested NLP libraries and was also a bit more difficult to handle when trying to force machine-readable output.<p>[1] <a href="https://www.mdpi.com/1999-5903/16/3/87" rel="nofollow">https://www.mdpi.com/1999-5903/16/3/87</a>
A lot of old stuff are still very relevant, they are much resource efficient, smaller amount of RAM, CPU, performance that's much faster. Easily 100x cheaper. I mean, if you have a few things you are trying to sort out, then using LLM to solve is okay, but if you have lots and lots of computation, then it's worth going classic.
I don't think there are any more which is a shame, because spaCy was an amazing library and probably the library I most ever enjoyed working with, it truly felt like a craftsmans belt for intelligent text transformation/insight. Some things like topic clouds can still be useful for creative work but this is not where spaCy shines.<p>But ChatGPT can derive better insights, doesn't need pipelines, doesn't need hard coded approaches with their issues. And the (NLTK/Stanford parser-like) dependency views are still interesting for linguistic purposes.