Course Preview: Natural Language Processing at CMU

[Video Transcript] | Natural Language Processing

Program Overview

Hello, and welcome to the program. I hope you're ready to begin. Now, to get started, let me tell you a little bit about what we're going to do in the program over the next few weeks. So first, it's composed of 10 separate modules, they're related, but there are 10 separate steps. We have activities and assessments. We want to be able to tell that you're learning, and we want you to be able to tell that you're learning. We'll give you the opportunity to practice these key concepts that we'll be telling you about. And it's not just what we are telling in the lecture, we want you to have some experience of hands-on actually doing that. In order to do that, there'll be programming assignments, so you'll be expected to implement and modify things with examples that we are going to give you so that you have that hands-on experience in dealing with natural language processing. And so ultimately, you'll be able to apply it to other tasks as well in your own task.


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We want you to get that experience in building these models, and running these models on tasks so you'll be able to do it in the future. So let's talk a little bit more about the program. What it's really about is advancing the knowledge of key principles that are part of natural language processing. As it's currently known, it's a subject that is improving all the time, and there'll be new things, but the intention is that you have that grounding and you'll be able to understand how it currently is, and hopefully in the future as it advances. Exploiting various computer models in NLP, and understanding both what they are and how you can actually do them in real-world problems and not just in the academic form. The work will be in Python, so you'll get a chance, the opportunity to actually build these programming exercises.

Our hope is that you'll learn how to successfully implement natural language algorithms themselves, but it also uses pre-existing, pre-trained models in such a way to help you solve these problems, okay? To accomplish this, we're going to focus on a number of different things. First, we're going to give a little bit of definitional space of what things are problems in NLP, and natural language processing so that you can identify what these tasks actually are. We're going to teach you and allow you to understand the internal word structure through learning about morphology so that you can apply techniques from there, such as being able to do stemming and lemmatization of words. You're going to be able to learn how to build automatic classifiers for documents to be able to identify topics, sentiment, et cetera, given some sets of documents. You'll also be able to understand neural networks and how they're currently used in natural language processing, how to be able to build on top of neural network models, and be able to both train and use them to make things better.

We also learn about computation models of labeling sequences of things, text, words, and speech, and how to be able to identify taggers that will identify names, places, parts of speech, et cetera. You'll also be able to learn how to represent words in an efficient way, using what's called embeddings, so that you can use it in those downstream tasks. So we can get a representation of words that allows us to be able to train models in an efficient way rather than just looking at the isolated words themselves. We're also going to look at the meaning of words and try to get meaning representations particularly of words first and be able to use that in our downstream tasks. And then later, we're going to look at the meanings of sentences, how these words combine together, and take advantage of the synthetic structure and parsing to find that synthetic structure, so we get the meaning and representation of whole sentences.

As we move through the program, remember, you will be part of a global cohort, and you should take advantage of it because different people in your cohort will have different expertise, and you can learn from them and use this as an opportunity to network. The program includes 24-hour support for technical issues, so don't let that hold you back. We also have experienced learning facilitators and you should take advantage of them. Use this opportunity to help get yourself through the course and learn. I hope you're ready to go, so let's get started.

[End of Video Transcript]

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