Here is the outline of an interesting course on deep learning in natural language processing (NLP), summarized from the syllabus of the course. The subtitles are given by me.
- Word vector: Vector representations of words are introduced, such as word2vec and GloVe
- Neural networks: introduction and backpropagation
- Introduction to Tensorflow
- Language structures and models
- Dependency parsing: identify which words depend on which other words
- Recurrent Neural Networks (RNNs) and Language Models
- State-of-the-art ideas and applications in NLP
- Machine Translation, Seq2Seq and attention (Lesson 10): introduce a new task (machine translation) that is the primary use-case of a new neural architecture (sequence-to-sequence, or Seq2Seq), which can be improved by a new neural technique (attention).
- Advanced attention
- Transformer Networks and convolutionary neural networks (CNNs)
- Conference resolution: identify all mentions that refer to the same real-world identity
- Advanced and relavant topics
- Reinforcement learning
- Semi-supervised and multi-task learning
- Future of NLP models, multi-task learning and QA systems
The course is now still ongoing. Interested students and professionals may wish to check the website of the course regularly for updated material.
Compared with the same course given last year, the material covered by the current course seems to remain comparable, though organisation of the material has been adapted to be more systematic, and to reflect new techniques.
How NLP can be useful for a bioinformatician?
A recent example is to use NLP techniques to determine semantic similarities between Gene Ontology (GO) terms.
Check out the publication on bioaRxiv: Duong, Dat, Wasi Uddin Ahmad, Eleazar Eskin, Kai-Wei Chang, and Jingyi Jessica Li. “Word and Sentence Embedding Tools to Measure Semantic Similarity of Gene Ontology Terms by Their Definitions.” BioRxiv, March 2, 2018, 103648.
In fact, I was drawn to the course mentioned above by the github repository of the first author, Dat Duong, who visited a earlier version of the course and setup interesting tools for example to compare two GO terms or two genes.
I would like to thank Richard Socher, the course’s instructor, and many teaching assitants who have not only designed and implemented such an interesting course, but also made it publicly available.