CC6205 - Natural Language Processing

This is a course on natural language processing.

• Lecturer: Felipe Bravo-Marquez

• TAs: Pablo Badilla, Gabriel Chaperón, Cristián Tamblay

• Lectures: Tuesday 14:30 - 16:00, Thursday 14:30 - 16:00 (Lecture Room B04, Beauchef 851, Edificio Poniente)

• Course Program (in Spanish)

• Course Calendar

• Youtube Playlist with lectures

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), video 1, video 2
3. Language Models (slides by Michael Collins), notes, video 1, video 2, video 3, video 4
4. Text Classification and Naive Bayes (slides by Dan Jurafsky), notes, video 1, video 2, video 3
5. Linear Models | (tex source file), video 1, video 2, video 3, video 4
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), video
11. Recurrent Neural Networks | (tex source file), video 1, video 2, video 3
12. Sequence to Sequence Models, Attention, and the Transformer | (tex source file), video 1, video 2, video 3
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. The Illustrated Transformer: a very illustrative blog post about the Transformer
13. ELMO explained
14. BERT exaplained
15. Better Language Models and Their Implications OpenAI Blog
16. David Bamman NLP Slides @Berkley
17. RNN effectiveness
18. SuperGLUE: an benchmark of Natural Language Understanding Tasks
19. decaNLP The Natural Language Decathlon: a benchmark for studying general NLP models that can perform a variety of complex, natural language tasks.
20. Deep Learning in NLP: slides by Horacio Rodríguez
21. Chatbot and Related Research Paper Notes with Images
22. XLNet Explained
23. PyTorch-Transformers: a library of state-of-the-art pre-trained models for Natural Language Processing (NLP)
24. Ben Trevett's torchtext tutorials
25. PLMpapers: a collection of papers about Pre-Trained Language Models
26. The Illustrated GPT-2 (Visualizing Transformer Language Models)
27. Linguistics, NLP, and Interdisciplinarity Or: Look at Your Data, by Emily M. Bender
28. The State of NLP Literature: Part I, by Saif Mohammad
29. From Word to Sense Embeddings:A Survey on Vector Representations of Meaning
30. 10 ML & NLP Research Highlights of 2019 by Sebastian Ruder
31. Towards a Conversational Agent that Can Chat About…Anything
32. Dive into Deep Learning Book
33. Stanza - A Python NLP Library for Many Human Languages

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. Computational Linguistics I by Jordan Boyd-Graber University of Maryland
6. Visualizing and Understanding Recurrent Networks
7. BERT Research Series by Chris McCormick
8. Successes and Challenges in Neural Models for Speech and Language - Michael Collins