LIN 389C: Reading list

Graphical models

Koller/Friedman Probabilistic Graphical Models (copies can be made available for class)
  • Introduction
  • Bayes Nets: directed graphical models
  • Markov Networks: undirected graphical models

Deep learning


  • Carlota Smith's work on modes of discourse.
    Also this: Friedrich, A., Palmer, A., and Pinkal, M. (2016): Situation entity types: automatic classification of clause-level aspect. Proceedings of ACL 2016.
  • Event schema learning: Work by Nate Chambers, Michaela Regneri, and our very own Karl Pichotta
  • The Penn Discourse Treebank

Single-document understanding

Single-document understanding is about doing in-depth analysis of a single document in order to extract as much information as possible about it. We are particularly interested in models that also use probabilistic inference. This reading list hence comprises approaches that can be called single-document understanding and approaches that use relevant technology.

Linguistics in computational linguistics

Check the publications lists of, for example:

Other topics that you mentioned (without readings yet, but with the name of the person who proposed the topic):

  • Computational psycholinguistics (Alex)
  • Simulated language learning through robots (Alex)
  • Question answering (Su)
  • Semantic parsing (Su)