The focus of the course will be on deep learning methods for learning linguistic representations, but standard discriminative and unsupervised methods will also be introduced. We will cover word embeddings, feed-forward neural networks, recurrent neural networks, recursive neural networks, and convolutional neural networks. In addition to the relevant architectures and learning algorithms, we will introduce deep learning approaches to a range of natural language understanding tasks, including language modeling, part-of-speech tagging, parsing, question answering, semantic role labeling, semantic composition, sentiment analysis, and discourse coherence.
The course uses a Piazza discussion forum for questions relating to the course material or the assignments. If you are enrolled in the course, you should have received an invitation to join Piazza in week 2. Contact the lecturers if you haven't.
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