Sequence Models

N-gram Language Models

Text classification

Word Embeddings

Constituency Parsing

Dependency Parsing

Recurrent neural network (RNN)

Machine Translation

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

Natural Language Processing

Sequence Models

1 courses cover this concept

COS 484: Natural Language Processing