Phil 11.7.16

6:30 – 3:00 ASRC

  • Notes from Aaron to discuss today:
    • http://karpathy.github.io/2015/05/21/rnn-effectiveness/?branch_used=true Great article on RNN. Sample code available too.

    • Slider based decisions for clustering topic models where we weight similarity contributions individually, including entities (who the document is about via NLP extraction), BOW comparison, TF-IDF LS comparison, etc. The clusters change based off the combined contribution of each vector of attractors.
  • Starting review of Novelty Learning via Collaborative Proximity Filtering
  • LingPipe is tool kit for processing text using computational linguistics. LingPipe is used to do tasks like:
    • Find the names of people, organizations or locations in news
    • Automatically classify Twitter search results into categories
    • Suggest correct spellings of queries
  • GATE is open source software capable of solving almost any text processing problem
  • Semantic Vectors creates semantic WordSpace models from free natural language text. Such models are designed to represent words and documents in terms of underlying concepts. They can be used for many semantic (concept-aware) matching tasks such as automatic thesaurus generation, knowledge representation, and concept matching.
  • LSA-based essay grading – could be good for document classification/spam detection
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