07. NLP Resources

Here are some additional resources that will help you design innovative models for NLP tasks:

  • Sebastian Ruder, 2017. Deep Learning for NLP Best Practices: Talks about several cutting-edge mechanisms being developed for NLP and how to best apply them, such as: Multi-Task Learning, Attention, Hyperparameter Optimization, Ensembling.

  • Chris Manning and Richard Socher, 2017. Natural Language Processing with Deep Learning (course).

    Great for learning about: Advanced Word Embeddings, Dependency Parsing, Coreference Resolution, Gated Recurrent Units.

  • Dan Jurafsky and James H. Speech and Language Processing, 2nd ed. [3rd ed. drafts | 2017 course]

    A comprehensive study of language processing and the related fields of speech recognition and synthesis. Covers in depth: Statistical Parsing, Information Retrieval, Question-Answering, Summarization.

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