corenlp

The best or the best is CoreNLP, but that's GPL so unlikely to be in your product unless you've licensed it.

That CoreNLP model was published late last year; before that, CoreNLP only had the older polynomial-time parsing algorithms implemented, which are much slower, and often less accurate.

But, possibly the CoreNLP parser has to do some contortions to integrate into their framework.

In particular, it would be good to know whether CoreNLP is doing more processing than spaCy or otherwise handling more concerns.

I'd be very curious to see performance/accuracy charts on a number of corpora in comparison to CoreNLP.

The new CoreNLP model uses greedy shift-reduce dependency parsing, same as spaCy.

For your other question, about what spaCy offers and what CoreNLP offers.

The reason that I find it funny is that The Stanford Parser/Stanford CoreNLP library is one of the more popular toolkits.

Finally, I'd really love to see a feature table comparing spaCy with CoreNLP.

I had a few weeks ago the problem of not being able to get a passive verb parser in CoreNLP fast enough to work.

There are also Python wrappers for Stanford CoreNLP.

At least, that's how I run the CoreNLP in this benchmark.

CoreNLP (which isn't as good as our NLP but is the "standard" only gets the parts of speech right 95% of the time, by words, but about 75% of the time by sentence.