November 2016
Beginner to intermediate
687 pages
15h 31m
English
book
Natural Language Processing: Python and NLTK
by Nitin Hardeniya, Jacob Perkins, Deepti Chopra, Nisheeth Joshi, Iti Mathur
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Your Turn
Here are the answers to the questions posed in the above sections:
- Can we remove stop words before POS tagging?
No; If we remove the stop words, we will lose the context, and some of the POS taggers (Pre-Trained model) use word context as features to give the POS of the given word.
- How can we get all the verbs in the sentence?
We can get all the verbs in the sentence by using
pos_tag>>>tagged = nltk.pos_tag(word_tokenize(s)) >>>allverbs = [word for word,pos in tagged if pos in ['VB','VBD','VBG'] ]
- Can you modify the code of the hybrid tagger in the N-gram tagger section to work with Regex tagger? Does that improve performance?
Yes. We can modify the code of the hybrid tagger in the N-gram tagger section to work with the Regex tagger:
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.