noliverop / CC6205

Natural Language Processing

CC6205 - Natural Language Processing

This is a course on natural language processing.

• Lecturer: Felipe Bravo-Marquez

• TAs: Gabriel Iturra, Ignacio Meza, María José Zambrano, Ricardo Córdova and Pablo Torres.

• Lectures: Tuesday 14:30 - 16:00, Thursday 14:30 - 16:00

• 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), video 1, video 2
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), video 1, video 2, video 3, video 4
7. Word Vectors | (tex source file) video 1, video 2, video 3
8. Tagging, and Hidden Markov Models (slides by Michael Collins), notes, video 1, video 2, video 3, video 4
9. MEMMs and CRFs | (tex source file), notes 1, notes 2, video 1, video 2, video 3
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. Contextual Words Representations (slides by Chris Manning) video 1, video 2, video 3, notes
14. 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
15. Recursive Networks and Paragraph Vectors | (tex source file)

NLP Libraries

1. NLTK: Natural Language Toolkit
2. Gensim
3. spaCy: Industrial-strength NLP
4. Torchtext
5. AllenNLP: Open source project for designing deep leaning-based NLP models
6. Transformers: a library of state-of-the-art pre-trained models for Natural Language Processing (NLP)
7. Stanza - A Python NLP Library for Many Human Languages
8. FlairNLP: A very simple framework for state-of-the-art Natural Language Processing (NLP)
9. WEFE: The Word Embeddings Fairness Evaluation Framework
10. WhatLies: A library that tries help you to understand. "What lies in word embeddings?"
11. LASER:a library to calculate and use multilingual sentence embeddings
12. Sentence Transformers: Multilingual Sentence Embeddings using BERT / RoBERTa / XLM-RoBERTa & Co. with PyTorch
13. Datasets: a lightweight library with one-line dataloaders for many public datasets in NLP

Notes and Books

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. NLTK book
7. Embeddings in Natural Language Processing by Mohammad Taher Pilehvar and Jose Camacho-Collados
8. Dive into Deep Learning Book

Other NLP Courses

1. CS224n: Natural Language Processing with Deep Learning, Stanford course
2. Deep Learning in NLP: slides by Horacio Rodríguez
3. David Bamman NLP Slides @Berkley
4. CS 521: Statistical Natural Language Processing by Natalie Parde, University of Illinois
5. 10 Free Top Notch Natural Language Processing Courses

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
9. More on Transforemers: BERT and Friends by Jorge Pérez

Other Resources

1. ACL Portal
2. Awesome-nlp: A curated list of resources dedicated to Natural Language Processing
3. NLP-progress: Repository to track the progress in Natural Language Processing (NLP)
4. NLP News By Sebastian Ruder
5. Corpora Mailing List
6. Real World NLP Book: AllenNLP tutorials
7. Attention is all you need explained
8. The Illustrated Transformer: a very illustrative blog post about the Transformer
9. ELMO explained
10. BERT exaplained
11. Better Language Models and Their Implications OpenAI Blog
12. RNN effectiveness
13. SuperGLUE: an benchmark of Natural Language Understanding Tasks
14. decaNLP The Natural Language Decathlon: a benchmark for studying general NLP models that can perform a variety of complex, natural language tasks.
15. Chatbot and Related Research Paper Notes with Images
16. XLNet Explained
17. Ben Trevett's torchtext tutorials
18. PLMpapers: a collection of papers about Pre-Trained Language Models
19. The Illustrated GPT-2 (Visualizing Transformer Language Models)
20. Linguistics, NLP, and Interdisciplinarity Or: Look at Your Data, by Emily M. Bender
21. The State of NLP Literature: Part I, by Saif Mohammad
22. From Word to Sense Embeddings:A Survey on Vector Representations of Meaning
23. 10 ML & NLP Research Highlights of 2019 by Sebastian Ruder
24. Towards a Conversational Agent that Can Chat About…Anything
25. The Super Duper NLP Repo: a collection of Colab notebooks covering a wide array of NLP task implementations
26. The Big Bad NLP Database, a collection of nearly 300 well-organized, sortable, and searchable natural language processing datasets
27. A Primer in BERTology: What we know about how BERT works
28. How Self-Attention with Relative Position Representations works
29. Deep Learning Based Text Classification: A Comprehensive Review
30. Teaching NLP is quite depressing, and I don't know how to do it well by Yoav Goldberg
31. The NLP index
32. 100 Must-Read NLP Papers