Bling Fire: Finite state machine and regular expression manipulation library

Re:Bling Fire: Finite state machine and regular expression m... by lettergram on Wednesday April 17, 2019 @11:31PM (#19688500)

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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Re:Bling Fire: Finite state machine and regular expression m... by avinium on Thursday April 18, 2019 @12:09AM (#19688645)

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.

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Re:Bling Fire: Finite state machine and regular expression m... by stochastic_monk on Thursday April 18, 2019 @08:16AM (#19690418)

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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