Python and relevant packages

• Python: https://www.python.org/downloads/ Use 3.[something]
• Setting up Python on a ChromeBook
• Using Python online: https://repl.it/languages/Python3
• An IDE for Python: PyCharm, https://www.jetbrains.com/pycharm/
• NLTK: Installing NLTK itself: http://www.nltk.org/install.html You also need the NLTK data, see http://www.nltk.org/data.html
• gensim: https://radimrehurek.com/gensim/
  

Learning Python:

• How To Think Like A Computer Scientist is a very good and accessible online Python textbook.
  (Caution: It uses Python 2 rather than Python 3. One main difference you will note is that they omit the () around the print() command.)
  
• A Python tutorial
• SoloLearn (available on the web and also as a mobile app): https://www.sololearn.com/Course/Python/
• CS41 course website from Stanford: https://stanfordpython.com/#resources
  
  
  

 Tutorials and texts about distributional models

• Jurafsky and Martin, chapter 6 of their upcoming 3rd edition of Speech and Language Processing
• Count-based models:
• Turney and Pantel overview article
• Another overview article is available on Canvas.
  
  
• Neural network based, deep learning models:
  
• The illustrated word2vec
• word2vec tutorial for gensim
  
• Book by Goodfellow, Bengio and Courville (text available online)
• 2013 deep learning tutorial by Socher, Manning and Bengio
  

• Latent Dirichlet Allocation (LDA) topic models:

• Mark Steyvers & Thomas Griffiths (2007), Probabilistic topic models
• Kevin Knight (2009), Bayesian Inference with Tears
• Alexander Koller, LDA with pirates and ninjas, and non-parametric Bayesian models with pirates and ninjas
  

Pre-computed spaces

• Start here: a collection of many pre-computed spaces
  
• Word2Vec
• GloVE
• TypeDM
  

Logic-based semantics in the Natural Language Toolkit

• NLTK book chapter: Analyzing semantics in the Natural Language Toolkit
• A paper by the authors of the semantics part of NLTK: Dan Garrette and Ewan Klein (2009), An Extensible Toolkit for Computational Semantics. Proceedings of IWCS. 
• NLTK howto: Semantics
• Discourse representation (DRT) in NLTK: the package
  
• NLTK API
  

Some freely available corpora

Data that can be used to build distributional models:

• The WaCKy corpora, including UKWaC (English web text, 2B words), Wackypedia (an English Wikipedia dump, 2B words), web corpora for French, German, and Italian. Ask me about a parsed version of UKWaC and Wackypedia if you need syntactic analysis.
  
• Catalan/Spanish/English wikicorpus

Deep semantic annotation:

• The Groningen Meaning Bank
• Its successor, the Parallel Meaning Bank
  

Systems and online demos for logic-based semantic analysis

• LinGO English Resource Grammar: demo that generates Minimal Recursion Semantics representations
• Theorem proving (automatic inference):  Prover9 and Mace4

Additional readings about logic-based computational semantics

An in-depth overview of everything:

• Jurafsky and Martin, chapter 16 of their upcoming 3rd edition of Speech and Language Processing
  

Taking more of a practical perspective:

• Blackburn and Bos: Representation and Inference for Natural Language,
   table of contents here
  
• van Eijck and Unger: Computational Semantics with Functional Programming

Focusing on the theory:

• L.T.F Gamut: Logic, Language, and Meaning (2 volumes)