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It is crazy slow and uses absurd amounts of memory, as well as being simultaneously an object lesson in both over-engineering, and under-engineering. Built by academics, so it's not unexpected.

Nice example of the application of recent NLP thesis topics to parsing problems, though. Quite suitable as documentation and reference for, e.g. future work on SpaCy - which is much more usable in productive contexts.

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Yes, it does use a reasonable chunk of memory (I wouldn't run in with less than 3GB available)

However, speed wise, its only slow in my experience, if you try to integrate via a shell script - if you run it as a service and make http calls it's been amazingly fast in my usage.

I believe they include a "service" mode as part of the package now, but for a project a little while ago I forked & extended a project that provides a JSON/XML http service: https://github.com/Koalephant/StanfordCoreNLPHTTPServer

In my testing it struck a good balance between accuracy and performance. I was also impressed by the lengths the group goes to help people on their mailing list. That said, their licensing is not friendly so I generally have to rule it out.

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