Models for Language Processing

Course goals

The course gives an overview of select natural language processing (NLP) tasks such as parsing, language modeling, and Machine Translation.
At the end of the course, students will understand the nature of linguistic challenges inherent in these tasks, as well as theoretical notions and computational methods applied to them.

Content

This course is part of the track Learning and Computation.

This course will focus on modern machine learning based and statistical systems and models for processing natural language. The course will cover advanced formalisms and techniques (both neural and statistical, as appropriate) for predicting syntactic structure, semantic structure, as well as models for machine translation. A portion of the course will be devoted to applications of language processing that are most commonly used in industry (e.g. models of sentiment analysis), and some unsupervised techniques like topic modelling may be included. Together these parts will provide students with an overview of the theory and practice in current data-driven natural language processing, and provide the building blocks for structure processing, language understanding and machine translation systems.

Additional information