Texts about logic-based computational semantics

Taking more of a practical perspective:

• From the NLTK book: Analyzing the Meaning of Sentences
• Blackburn and Bos: Representation and Inference for Natural Language
• van Eijck and Unger: Computational Semantics with Functional Programming

Focusing on the theory:

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

Online demos of logic-based analyses

• Boxer: scroll to the bottom to generate DRSs for arbitrary English sentences
• LinGO English Resource Grammar: generate MRS representations
  

Toolkits for computing distributional models

• dv: computing distributional models in which the dimensions are dependency parse snippets
• the DISSECT toolkit for computing distributional models and for doing compositional distributional semantics

NLTK

• For the logic-based semantics, we use Python and the Natural Language Toolkit (NLTK)
  
• The theorem provers that NLTK uses:  Prover9 and Mace4