Language Technologies I teaches the theoretical foundation of modern computational linguistics. This includes important machine learning techniques.

Outline

1. Formal models of language: possibilities
   HW1
2. Statistical models of language: probabilities
   HW2
3. Applications of language models
   HW3
4. n-gram language models and smoothing, by Mittul Singh
   HW4
5. Parts of speech, word clusters, and class-based language models
   HW5
6. Log-linear models
7. Word vectors, and applications
   HW6
8. Dependency parsing, by Prof. Günter Neumann
   HW7 (optional)
9. Probabilistic context-free grammars and syntactic language models
   HW8
10. Feedforward neural networks and autoencoders
    HW9
11. Recurrent neural networks and their language models
    HW10 (updated)
12. Sequence-to-sequence models and neural machine translation
13. Convolutional networks and character-based models of language

External Links

• List of Language Modeling Software
• Neural Network Software:
• Keras - You can install it by typing the following from the command line:
  

  pip install --user keras

• List of other software for neural networks
• Another list of recurrent neural network stuff
• Free Corpora:
• WMT 2016
• ACL Wiki, "Resources by Language"
• OPUS - open parallel corpora
• Corpus Processing Tools:
• https://github.com/jonsafari/habeas-corpus
• https://github.com/kpu/preprocess
• http://corpus.tools