checopa/CC6205

Natural Language Processing

CC6205 - Natural Language Processing

This is a course on natural language processing.

• Lecturer: Felipe Bravo-Marquez

• TAs: Pablo Badilla, Jocelyn Durstan, Juglar Díaz

• Lectures: Tuesday 14:30 - 16:00, Thursday 14:30 - 16:00 (Lecture Room B104, Beauchef 851, Piso 1, Edificio Norte)

• Course Program (in Spanish)

• Course Calendar

Info

The neural network-related topics of the course are taken from the book of Yoav Goldberg: Neural Network Methods for Natural Language Processing. The non-neural network topics (e.g., grammars, HMMS) are taken from the course of Michael Collins.

Slides

1. Introduction to Natural Language Processing | (tex source file)
2. Vector Space Model and Information Retrieval | (tex source file)
3. Language Models (slides by Michael Collins), notes, videos 1, videos 2, videos 3
4. Text Classification and Naive Bayes (slides by Dan Jurafsky), notes, video 1, video 2, video 3, video 4, video 5, video 6, video 7, video 8, video 9
5. Linear Models | (tex source file)
6. Neural Networks | (tex source file)
7. Word Vectors | (tex source file)
8. Tagging, and Hidden Markov Models (slides by Michael Collins), notes, videos
9. MEMMs and CRFs | (tex source file)
10. Convolutional Neural Networks | (tex source file)
11. Recurrent Neural Networks | (tex source file)
12. Sequence to Sequence Models | (tex source file)
13. Constituency Parsing slides 1, slides 2, slides 3, slides 4 (slides by Michael Collins), notes 1, notes 2, videos 1, videos 2, videos 3, videos 4
14. Recursive Networks and Paragraph Vectors | (tex source file)

Other Resources

1. Speech and Language Processing (3rd ed. draft) by Dan Jurafsky and James H. Martin.
2. Michael Collins' NLP notes.
3. A Primer on Neural Network Models for Natural Language Processing by Joav Goldberg.
4. Natural Language Understanding with Distributed Representation by Kyunghyun Cho
5. Natural Language Processing Book by Jacob Eisenstein
6. CS224n: Natural Language Processing with Deep Learning, Stanford course
7. NLP-progress: Repository to track the progress in Natural Language Processing (NLP)
8. NLTK book
9. AllenNLP: Open source project for designing deep leaning-based NLP models
10. Real World NLP Book: AllenNLP tutorials
11. Attention is all you need explained
12. ELMO explained
13. BERT exaplained
14. Better Language Models and Their Implications OpenAI Blog
15. David Bamman NLP Slides @Berkley
16. RNN effectiveness
17. SuperGLUE: an benchmark of Natural Language Understanding Tasks
18. decaNLP The Natural Language Decathlon: a benchmark for studying general NLP models that can perform a variety of complex, natural language tasks.
19. Deep Learning in NLP: slides by Horacio Rodríguez
20. Chatbot and Related Research Paper Notes with Images
21. XLNet Explained
22. PyTorch-Transformers: a library of state-of-the-art pre-trained models for Natural Language Processing (NLP)

Videos

1. Natural Language Processing MOOC videos by Dan Jurafsky and Chris Manning, 2012
2. Natural Language Processing MOOC videos by Michael Collins, 2013
3. Natural Language Processing with Deep Learning by Chris Manning and Richard Socher, 2017
4. CS224N: Natural Language Processing with Deep Learning | Winter 2019
5. Visualizing and Understanding Recurrent Networks