Contextualized representations (BERT)

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

Verb / sentence semantics

Compositional semantics

Semantic Parsing

Discourse

Entity linking

Pragmatics

Speech

Word Embeddings

Natural Language Generation

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

Contextualized representations (BERT)

1 courses cover this concept

11-411/611 Natural Language Processing