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So, how does this compare to Pattern (http://www.clips.ua.ac.be/pages/pattern) -- a (in my opinion) very high-quality, BSD-licensed data mining and NLP library coming from the academic world?

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From a quick speed test on my laptop, Pattern is 48x slower at POS tagging, and 8x slower at parsing. I last benchmarked its accuracy in 2013, where I found it got 93.5% on the WSJ corpus, vs 97% for the state-of-the-art taggers --- so twice as many errors. It was also more domain-dependent. Its parser doesn't produce exactly the same representations as mine, so I can't easily evaluate its accuracy. But, I doubt it's very high.

Pattern doesn't really use machine learning, just some pre-computed statistics from the annotated data, and some hand-crafted rules. Machine learning is good. It's really the right way to build these systems.

GitHub link of Pattern - https://github.com/clips/pattern

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