Text classification

Language Modeling

Vector Embeddings

PyTorch

Finite-state machine

Sequence Labelling

Machine Translation

Sequence-to-sequence (Seq2Seq)

Morphology

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

Natural Language Processing

Regular Expressions

Minimum Edit Distance

Naive Bayes

Sentiment Analysis

Logistic Regression

Information Retrieval

Vector Semantics and Embeddings

Chatbots

Web graphs, Links, and PageRank

Social Networks

Neural network

CS 124: From Languages to Information Stanford University Winter 2023 This course is centered on extracting information from unstructured data in language and social networks using machine learning tools. It covers techniques like sentiment analysis, chatbot development, and social network analysis. The online world has a vast array of unstructured information in the form of language and social networks. Learn how to make sense of it using neural networks and other machine learning tools, and how to interact with humans via language, from answering questions to giving advice! Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (string algorithms, edit distance, language modeling, machine learning, logistic regression, neural networks, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, text classification, social networks, recommender systems), and ethical issues. CS106B. CS 107 can be helpful, but is fine if you haven't had it, we'll cover the required UNIX material. Math 51 can also be helpful, but isn't required, since we will introduce the basic vectors knowledge we need in the class. #### Textbook

There is no required textbook, but I'll expect you to know the textbook/reading material listed above, and will test it on the midterms.
- Online new chapters from [Jurafsky and Martin. third edition in progress. *Speech and Language Processing*](https://web.stanford.edu/~jurafsky/slp3/).
- Chapters from Manning, Raghavan, and Schutze. 2008. Introduction to Information Retrieval. Cambridge University Press. You can buy the book, get it from the library, or it's also available online [*HERE*](https://site.ebrary.com/lib/stanford/docDetail.action?docID=10240274).

**Is 106B the only prereq? Do I need 109 or 221 before I take CS124?** : 106B is the only prereq. Taking the course as a sophomore is recommended, but we also get lots of juniors and a reasonable number of frosh; the course is designed to be taken early in your Stanford career. It will help if you have at least done some programming beyond 106B, and is also useful to have had 107 or Math 51, but not required; we'll try to give you pointers to places to make up missing background.

**Can I take this course as a non-CS grad student?**: Yes, although this course is not appropriate for CS grad students (there are graduate versions of all the material in this course), it's very commonly taken by PhD students in the social sciences or humanities who plan to use text processing methods in their research.

CS 124: From Languages to Information

N-gram Language Models

Word Embeddings

Sequence Models

Constituency Parsing

Dependency Parsing

Recurrent neural network (RNN)

Transformer (machine learning model)

Contextualized Representations and Pre-training

Large Language Model (LLM)

Question Answering (QA)

Long Short-Term Memory (LSTM)

COS 484: Natural Language Processing Princeton University Spring 2023 This course introduces the basics of NLP, including recent deep learning approaches. It covers a wide range of topics, such as language modeling, text classification, machine translation, and question answering. ### What is this course about?

Recent advances have ushered in exciting developments in natural language processing (NLP), resulting in systems that can translate text, answer questions and even hold spoken conversations with us. This course will introduce students to the basics of NLP, covering standard frameworks for dealing with natural language as well as algorithms and techniques to solve various NLP problems, including recent deep learning approaches. Topics covered include language modeling, representation learning, text classification, sequence tagging, syntactic parsing, machine translation, question answering and others.  - Required: [COS 324](https://www.cs.princeton.edu/courses/archive/fall21/cos324/), knowledge of probability, linear algebra, multivariate calculus.
- Proficiency in Python: programming assignments and projects will require use of Python, Numpy and PyTorch. ### Reading:

There is no required textbook for this class, and you should be able to learn everything from the lectures and assignments. However, if you would like to pursue more advanced topics or get another perspective on the same material, here are some books (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)
- Christopher Manning and Hinrich Schütze. [Foundations of Statistical Natural Language Processing](https://nlp.stanford.edu/fsnlp/).

COS 484: Natural Language Processing

Text classification

3 courses cover this concept

CSE 447 and 517 Natural Language Processing

CS 124: From Languages to Information

COS 484: Natural Language Processing