Compare Stanford CoreNLP and spaCy 
Stanford CoreNLP vs spaCy
- Reviewers felt that spaCy meets the needs of their business better than Stanford CoreNLP.
- When comparing quality of ongoing product support, reviewers felt that spaCy is the preferred option.
- For feature updates and roadmaps, our reviewers preferred the direction of spaCy over Stanford CoreNLP.
I've used the Stanford NLTK parse is various natural language processing projects. It works very well, documentation is good enough.
The lack of good support of non-English languages
I like the sentence parser and the quality of sentences I generates. I like the object based approach so it is very easy for making a flow. SpaCy also solve many problems with its trained models that other libraries can't.
I dislike its user interface, it needs to be updated with something that is more intuitive and user-friendly
I've used the Stanford NLTK parse is various natural language processing projects. It works very well, documentation is good enough.
I like the sentence parser and the quality of sentences I generates. I like the object based approach so it is very easy for making a flow. SpaCy also solve many problems with its trained models that other libraries can't.
The lack of good support of non-English languages
I dislike its user interface, it needs to be updated with something that is more intuitive and user-friendly
