Natural Language Generation

Information extraction

Words

Language models and smoothing

Lexicons

Morphology

Question Answering (QA)

Noisy channel models

Edit Distance

Part of speech tags

Sequence Tagging

Classification

Deep learning

Syntactic representations of natural language

Chomsky hierarchy and natural language

Treebanks

Lexical semantics

Vector semantics

Contextualized representations (BERT)

Verb / sentence semantics

Compositional semantics

Semantic Parsing

Discourse

Entity linking

Pragmatics

Speech

Word Embeddings

Machine Translation

11-411/611 Natural Language Processing Carnegie Mellon University Spring 2021 Focused on computational systems for human languages, this course introduces various NLP applications, such as translation and summarization. It encompasses a broad scope, from machine learning to linguistics, with a software engineering perspective. This course is about a variety of ways to represent human languages (like English and Chinese) as computational systems, and how to exploit those representations to write programs that do neat stuff with text and speech data, like

- translation,
- summarization,
- extracting information,
- question answering,
- natural interfaces to databases, and
- conversational agents.

This field is called Natural Language Processing or Computational Linguistics, and it is extremely multidisciplinary. This course will therefore include some ideas central to Machine Learning and to Linguistics.

We'll cover computational treatments of words, sounds, sentences, meanings, and conversations. We'll see how probabilities and real-world text data can help. We'll see how different levels interact in state-of-the-art approaches to applications like translation and information extraction.

From a software engineering perspective, there will be an emphasis on rapid prototyping, a useful skill in many other areas of Computer Science. ### Should I take this course?

Yes, if:

- you're a CS student interested in languages, language technology, or information processing
- you're a CS student who needs an "applications" credit
- you're a language technology minor (this course is an elective option)
- you're a linguistics student who can write computer programs (this course is an elective option)
- you always suspected natural language was kind of like Lisp (or Java or ...)
- you want computers to take over the world
- you don't want computers to take over the world, but if they do, you want to negotiate your release
- you like AI, machine learning, and/or theoretical computer science, and want to apply them to a hard real-world problem CS courses on data structures and algorithms, and strong programming skills. [Speech and Language Processing](https://home.cs.colorado.edu/~martin/slp2.html) (2nd Edition, 2007, Prentice-Hall), by Daniel Jurafsky and James Martin

### FAQ
Should I take this course?

Yes, if:

- you're a CS student interested in languages, language technology, or information processing
- you're a CS student who needs an "applications" credit
- you're a language technology minor (this course is an elective option)
- you're a linguistics student who can write computer programs (this course is an elective option)
- you always suspected natural language was kind of like Lisp (or Java or ...)
- you want computers to take over the world
- you don't want computers to take over the world, but if they do, you want to negotiate your release
- you like AI, machine learning, and/or theoretical computer science, and want to apply them to a hard real-world problem

11-411/611 Natural Language Processing

Natural Language Processing

Word Vectors

Word Window Classification

Language Models

Backpropagation

Neural network

Dependency Parsing

Recurrent neural network (RNN)

Sequence-to-sequence (Seq2Seq)

Subword Models

Self-Attention

Transformer (machine learning model)

Pretraining

Reinforcement Learning from Human Feedback (RLHF)

Convolutional neural network (CNN)

Tree Recursive Neural Networks

Constituency Parsing

Code Generation

Multimodal Deep Learning

Coreference Resolution

CS 224N: Natural Language Processing with Deep Learning Stanford University Winter 2023 CS 224N provides an in-depth introduction to neural networks for NLP, focusing on end-to-end neural models. The course covers topics such as word vectors, recurrent neural networks, and transformer models, among others. Natural language processing (NLP) is a crucial part of artificial intelligence (AI), modeling how people share information. In recent years, deep learning approaches have obtained very high performance on many NLP tasks. In this course, students gain a thorough introduction to cutting-edge neural networks for NLP. ## What is this course about?

Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, politics, etc. In the last decade, deep learning (or neural network) approaches have obtained very high performance across many different NLP tasks, using single end-to-end neural models that do not require traditional, task-specific feature engineering. In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP. Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models, using the Pytorch framework.

>  “Take it. CS221 taught me algorithms. CS229 taught me math. CS224N taught me how to write machine learning models.” – A CS224N student on Carta - **Proficiency in Python**
    All class assignments will be in Python (using NumPy and PyTorch). If you need to remind yourself of Python, or you're not very familiar with NumPy, you can come to the Python review session in week 1 (listed in the schedule). If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Java/Javascript), you will probably be fine.
- **College Calculus, Linear Algebra** (e.g. MATH 51, CME 100)
    You should be comfortable taking (multivariable) derivatives and understanding matrix/vector notation and operations.

- **Basic Probability and Statistics** (e.g. CS 109 or equivalent)
    You should know the basics of probabilities, gaussian distributions, mean, standard deviation, etc.

- **Foundations of Machine Learning** (e.g. CS221, CS229, CS230, or CS124)
    We will be formulating cost functions, taking derivatives and performing optimization with gradient descent. If you already have basic machine learning and/or deep learning knowledge, the course will be easier; however it is possible to take CS224n without it. There are many introductions to ML, in webpage, book, and video form. One approachable introduction is Hal Daumé’s in-progress A Course in Machine Learning. Reading the first 5 chapters of that book would be good background. Knowing the first 7 chapters would be even better! ### Reference Texts

The following texts are useful, but none are required. All of them can be read free online.

- Dan Jurafsky and James H. Martin. [Speech and Language Processing (3rd ed. draft)](https://web.stanford.edu/~jurafsky/slp3/)
- Jacob Eisenstein. [Natural Language Processing](https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf)
- Yoav Goldberg. [A Primer on Neural Network Models for Natural Language Processing](http://u.cs.biu.ac.il/~yogo/nnlp.pdf)
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. [Deep Learning](http://www.deeplearningbook.org/)
- Delip Rao and Brian McMahan. [Natural Language Processing with PyTorch](http://library.stanford.edu/sfx?genre=book&atitle=&title=Natural%20language%20processing%20with%20PyTorch%20:%20build%20intelligent%20language%20applications%20using%20deep%20learning%20/&isbn=9781491978207&volume=&issue=&date=20190101&aulast=Rao,%20Delip,,%20author.&spage=&pages=&sid=EBSCO:VLeBooks:edsvle.AH35866319) (requires Stanford login).
- Lewis Tunstall, Leandro von Werra, and Thomas Wolf. [Natural Language Processing with Transformers](https://transformersbook.com/)

If you have no background in neural networks but would like to take the course anyway, you might well find one of these books helpful to give you more background:

- Michael A. Nielsen. [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com)
- Eugene Charniak. [Introduction to Deep Learning](https://mitpress.mit.edu/books/introduction-deep-learning)

CS 224N: Natural Language Processing with Deep Learning