Morphology

Information extraction

Words

Language models and smoothing

Lexicons

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

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

Text classification

Language Modeling

Vector Embeddings

PyTorch

Finite-state machine

Sequence Labelling

Sequence-to-sequence (Seq2Seq)

Syntax

Conditional Random Fields (CRF)

Semantics

Linguistic Structure Prediction

Neural Machine Translation

Multinomial Logistic Regression

Weighted Finite-State Transducers

CSE 447 and 517 Natural Language Processing University of Washington Winter 2022 This course provides a comprehensive overview of Natural Language Processing (NLP), including core components like text classification, machine translation, and syntax analysis. It offers two project types: implementation problem-solving for CSE 447, and reproducing experiments from recent NLP papers for CSE 517.  Natural language processing (NLP) seeks to endow computers with the ability to intelligently process human language. NLP components are used in conversational agents and other systems that engage in dialogue with humans, automatic translation between human languages, automatic answering of questions using large text collections, the extraction of structured information from text, tools that help human authors, and many, many more. This course will teach you the fundamental ideas used in key NLP components. It is organized into several “greatest hits” topics, each with a more-or-less self-contained lecture and associated readings, problems, and implementation exercises.  The courses are similar in breadth and use the same lecture content. The projects are quite different; 447’s project is a predefined implementation problem that gives teams freedom in developing a solution. It is designed to encourage iterative improvement and an understanding of inherent tradeoffs in building an NLP system. 517’s project is more research-oriented; it asks teams to reproduce experiments in recently published NLP papers. Teams have great flexibility in the choice of a paper to reproduce.

Additionally, there will be differences in the assignments.

CSE 447 and 517 Natural Language Processing

Morphology (linguistics)

Morphology

2 courses cover this concept

11-411/611 Natural Language Processing

CSE 447 and 517 Natural Language Processing