The truth is nltk is basically crap for real work, but there's so little NLP software that's put proper effort into documentation that nltk still gets a lot of use.

You can work your way down the vast number of nltk modules, and you'll find almost none of them are useful for real work, and those that are, ship a host of alternatives that are all much worse than the current state-of-the-art.

nltk makes most sense as a teaching tool, but even then it's mostly out of date. The chapter on "Parsing" in the nltk book doesn't even really deal with statistical parsing. The dependency parsing work referenced in this post is almost all 1-3 years old, so obviously it isn't covered either.

As an integration layer, nltk is so much more trouble than it's worth. You can use it to compute some of your scoring metrics, or read in a corpus, but...why bother?

I'm slowly putting together an alternative, where you get exactly one tokeniser, exactly one tagger, etc. All these algorithms have the same i/o, so we shouldn't ask a user to choose one. We should just provide the best one.

My previous post, on POS tagging, shows that ntlk's POS tagger is incredibly slow, and not very accurate: https://honnibal.wordpress.com/2013/09/11/a-good-part-of-spe... . nltk scores 94% in 3m56s, my 200 line implementation scores 96.8% in 12s.

I used to use nltk for tokenisation and sentence-boundary detection, but this library seems better for that: https://code.google.com/p/splitta/

NLTK is a toolkit which includes a bunch of different tokenizers, parsers, taggers, stemmers, lemmatizers, and corpora. The Stanford parser which he references in this post is a parser which can be integrated to work with NLTK, but it seems the methods which he discusses are more accurate and much faster than anything included in NLTK.

Also, the work he describes requires that the text already be tokenize and POS-tagged which will add some time, but not too much (and are also functions which can be performed by NLTK).