Deep Learning for NLP
Organization
- Lecture: Dienstag 13:30-15:10, Room S101/A02
- Practice Class: Dienstag 15:20-17:00, Room S207/167
- Integrierte Lehrveranstaltung (TUCaN-ID 20-00-0947-iv)
The learning material is available from the Moodle eLeaning platform.
The required passcode will be distributed during the lecture.
The first lecture and the first practice class will be on April 12.
Exam
- Date/Time: July 28, 2016, 09:30 – 12:00
- Room: to be announced
Teaching Staff
- Steffen Eger (no fixed office hour, please just ask for an appointment per mail)
- Lisa Beinborn (no fixed office hour, please just ask for an appointment per mail)
Course Content
The lecture provides an introduction to the foundational concepts of deep learning and their application to problems in the area of natural language processing (NLP)
Main aspects:
- Foundations of deep learning (e.g. feed-forward networks, hidden layers, backpropagation, activation functions, loss functions)
- Word embeddings: theory, different approaches and models, application as features for machine learning
- Different architectures of neuronal networks (e.g. recurrent NN, recursive NN, convolutional NN) and their application for groups of NLP problems such as document classification (e.g. spam detection), sequence labeling (e.g. POS-tagging, Named Entity Recognition) and more complex structure prediction (e.g. Chunking, Parsing, Semantic Role Labeling)
Requirements:
foundations in math (linear algebra) and programming (python)
Literature
- Yoav Goldberg (2015): A Primer on Neural Network Models for Natural Language Processing, published on arxiv
- Lecture by Richard Socher: Deep Learning for Natural Language Processing
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning, Book in preparation for MIT Press
Expectations
What you can expect from us:
- interactive lecture with integrated tutorials
- problem-based and explorative learning
- stimulating environment
What we expect from you:
- commitment
- feedback
- active participation