

Natural Language Processing in Python - fheisler
http://engineroom.trackmaven.com/blog/monthly-challenge-natural-language-processing/

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andrewguenther
NLTK is a wonderful toolkit. Its selection of corpera is great and its many
utility functions for processing text are incredibly useful and easily
extendable.

That being said, a lot of the ML, porter, and stemmer implementations are a
bit out of date from the current cutting edge in the field. If you are
interested in using NLTK for serious projects, I highly recommend writing
custom implementations of these modules or using other libraries.

~~~
acosmism
Agreed. I too had given a tutorial on this a while back
([https://www.youtube.com/watch?v=kKe4M4iSclc](https://www.youtube.com/watch?v=kKe4M4iSclc))
and nltk is a quick way to prototype up something neat,but yes- if you need
more than "toy" functionality, there are currently better tools for the job.

~~~
bane
Thanks for the talk, I enjoyed the video. Do you have any good pointers to
building a named entity extractor with NLTK?

~~~
acosmism
Thank you, I'm glad you you enjoyed it - I hope to have one on more advanced
topics at some point. If you are looking for a named entity extractor sample,
I have a sample from my talk on github: [https://github.com/shanbady/NLTK-
Boston-Python-Meetup/blob/m...](https://github.com/shanbady/NLTK-Boston-
Python-Meetup/blob/master/named_ent_chunker.py)

The sample uses the built-in named entity tagger but nltk also has support for
leveraging the Stanford named entity tagger:
[http://www.nltk.org/api/nltk.tag.html#module-
nltk.tag.stanfo...](http://www.nltk.org/api/nltk.tag.html#module-
nltk.tag.stanford)

~~~
bane
Thanks for the links. Please put me on the list for when a video of your
second talk is out.

