NLTK has an active and growing developer community. We're grateful to Matthew Honnibal for permission to port his averaged perceptron tagger, and it's now included in NLTK 3.1.
Note that NLTK includes reference implementations for a range of NLP algorithms, supporting reproducibility and helping a diverse community to get into NLP. We provide interfaces for standard NLP tasks, and an easy way to switch from using pure Python implementations to using wrappers for external implementations such as the Stanford CoreNLP tools. We're adding "scaling up" sections to the NLTK book to show how this is done.
The second edition of the book will include a "scaling up" section in most chapters, which shows how to transition from NLTK's pure Python implementations to NLTK's wrappers for the Stanford tools.
Note that NLTK includes reference implementations for a range of NLP algorithms, supporting reproducibility and helping a diverse community to get into NLP. We provide interfaces for standard NLP tasks, and an easy way to switch from using pure Python implementations to using wrappers for external implementations such as the Stanford CoreNLP tools. We're adding "scaling up" sections to the NLTK book to show how this is done.
https://github.com/nltk/nltk | https://pypi.python.org/pypi/nltk | http://www.nltk.org/book_2ed/ch05.html#scaling-up