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One advantage is the core appears to be written in C/C++. NLTK is python and SpaCy is a lot of C/C++ with a significant amount of python.

I’ll have to dig in further, but I suspect there’s a lot of optimization for the task at hand here. SpaCy and NLTK tries to be much more general for NLP.

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I was more so referring to spacy, where (IIRC) a lot of the lower-level tokenization & parsing is written in Cython, so (I assume) the simple fact that Bling is written in C++ wouldn't explain the huge performance gains.

There’s still a good amount of overhead in Cython portions of code unless there are no python objects or GIL involved, and then there’s still overhead communicating between C and Python worlds.

That’s part of why I stopped writing Cython and just use pybind11.

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