Books

1. Speech and Language Processing by Jurafsky and Martin: 3rd Edition drafts. This edition covers the latest developments after the deep learning revolution that has invigorated NLP.
2. Natural Language Processing by Jacob Eisenstein.
3. Foundations of Statistical Natural Language Processing by Manning and Schutze (1999): website. A classic book that covers statistical techniques for NLP. Highly rated.
4. Natural Language Processing for Online Applications by Jackson and Moulinier (2007): Amazon. Good book to get an overview of how NLP is used in the industry. "Bed time reading".
5. Natural Language Processing in Python (NLTK Book — online).

Courses

1. CS224N: Natural Language Processing with Deep Learning by Chris Manning:
   • (2021) Videos (27 hrs), Website. Prereq: A Course in Machine Learning book by Hal Daume III.
   • (2017) Website, Videos (25 hrs), Slides.
   • (2019) Website, Videos (27 hrs), Slides.
2. CS124: From Languages to Information by Jurafsky (2019): website. This course is based on the 3rd Edition of Jurafski and Martin's book which covers Deep Learning.
3. Course by Jurafsky & Manning (2012): Youtube Playlist (100 videos) — Slides — Slides & Assignments.
4. Old CS224N: Natural Language Processing (2012 perhaps) by Manning: Videos. This is the "classic" CSS224N by Manning which covers statistical techniques.
5. Ling 289: Quantitative and Probabilistic Explanation in Linguistics (2007) by Chris Manning: Website.

Resources

1. Course Notes for NLP by Jason Eisner, 2017.
2. Natural Language Processing by Drogamir Radev (2016): Videos — Book (Graph-based Natural Language Processing and Information Retrieval, 2011) (a specialized book covering graph-based techniques).
3. NLP Progress — good collection of benchmarks and latest results.

Research Papers

1. List of good NLP papers (Quora).

Books

1. Introduction to Information Retrieval by Manning, Raghavan and Schutze (2008): online copy.

Courses

1. CS276: Information Retrieval and Web Search by Manning and Nayak (2017): Slides

Courses:

1. CS224U at Stanford: Natural Language Understanding: Website. No videos.

Resources:

1. Natural Language Understanding: Foundations and State of the Art by Percy Liang (2017). Slides. A good overview of NLU in 2017. Percy Liang also wrote Talking to Computers in Natural Language, XRDS, 2014 which is a quick summary of the history of NL Interfaces.

Books

1. Knowledge Representation and Reasoning by Brachman and Levesque (2004).
2. Knowledge Representation by John Sowa (1999). Website.
3. Knowledge Representation by Arthur Markman (2013).
4. Handbook of Knowledge Representation by Harmelen, Lifschitz and Porter (2008).
5. Thinking Fast and Slow by Daniel Kahneman (2013).

Courses

1. CS 227 at Stanford (2011): Website.

Papers

1. A translation approach to portable ontology specifications by Thomas Gruber, Knowledge Acquisition, Volume 5, Issue 2, June 1993, Pages 199-220.

Tutorials

A Primer on Neural Network Models for Natural Langauge Processing by Yoav Goldberg, Nov 2015. Appeared in J Artificial Research, Vol 57, 2016.

Books

Neural Network Methods in Natural Language Processing by Yoav Goldberg, 310 pages, 2017.

Surveys

Deep Learning NLP Best Practices.

Websites and Blogs

NLP Progress has a good classification of NLP problems.

GitHub: State of the Art in Machine Learning Problems.

ACL 2018 Semantic Parsing Tutorial.

Sebastian Ruder's Blog. For example, 10 Exciting Ideas of 2018 in NLP.

Learning Representations by Backpropagating Errors by D E Rumelhart, G E Hinton & R J Williams, Nature, Vol 3, p 533-536, 1986.

A Neural Probabilistic Language Model by Y Bengio, R Ducharme, P Vincent & C Jauvin, J Machine Learning Research, Vol 3, p 1137-1155, 2003.

Natural Language Processing (Almost) from Scratch by R Collobert, J Weston, L Bottou, M Karlen, K Kavukcuoglu & P Kuska, J Machine Learning Research, Vol 12, p 2493-2537, 2011.

Distributed Representations of Words and Phrases and their Compositionality by T Mikolov, I Sutskever, K Chen, G Corrado & J Dean, NIPS 2013.