Hey guys, and welcome back to this class, 2
"Data Science: Natural Language Processing in Python." 3
In this lecture, we're going to talk about what we didn't cover in 4
this course and what you can look for if you want to learn more about NLP. 5
So one thing is edit distance. 6
This is how you tell how different two strings are, for example, 7
how many additions, deletions, 8
and substitutions you have to do to get from the first string to the other string. 9
This has applications in 10
computational biology since DNA strands can be considered strings, 11
and it helps us to do interesting things like tell how related two species are. 12
This involves dynamic programming, 13
so you would learn about this in an algorithms class. 14
Parsing is a huge topic in NLP. 15
So we looked a little bit at how NLTK does this but we don't know how to do it ourselves. 16
This is basically determining 17
the structure of a sentence and breaking it down into a tree, 18
but we won't say much more than that. 19
We also didn't talk about MaxEnt classifiers, 20
which is common terminology in NLP. 21
This stands for maximum entropy. 22
And usually when this is taught in the context of NLP, 23
you define binary features like 'this term is a number or not', 24
'this term starts with a capital letter or not'. 25
Eventually, these handmade features are fed into a logistic regression model which – as 26
you know from my class and logistic regression – maximizes the likelihood of our model. 27
So really, this is just a fancy term for logistic regression. 28
In fact, you could even take this a step further by feeding the raw input 29
into a neural network so that the features could be learned automatically. 30
If you're interested in learning more, 31
I would recommend taking my Udemy class on logistic regression, 32
and you can find this at udemy.com/data-science-logistic-regression-in-python. 33
So what is the state of the art in NLP currently? 34
Most people have seen that deep learning is doing a very good job at NLP tasks. 35
With recurrent neural networks, 36
we are able to model sequences very accurately. 37
My intro to deep learning is also on Udemy, 38
and you can find it at udemy.com/data-science-deep-learning-in-python. 39
Thank you for taking this course, 40
and I hope you learned a lot.