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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Applying interpolation on data to get mix and match
The limitation of using an additive smoothed bigram is that we back off to a state of ignorance when we deal with rare text. For example, the word captivating occurs five times in a training data: thrice followed by by and twice followed by the. With additive smoothing, the occurrence of a and new before captivating is the same. Both the occurrences are plausible, but the former is more probable as compared to latter. This problem can be rectified using unigram probabilities. We can develop an interpolation model in which both the unigram and bigram probabilities can be combined.
In SRILM, we perform interpolation by first training a unigram model with -order 1 and –order 2 used for the bigram ...
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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.