Machine learning for natural language processing
The course gives an introduction to machine learning models and architectures used in modern natural language processing (NLP) systems.
Rapid developments in machine learning have revolutionized the field of NLP, including for commerically important applications such as translation, summarization, and information extraction. However, natural language data exhibit a number of peculiarities that make them more challenging to work with than many other types of data commonly encountered in machine learning: natural language is discrete,structured, and highly ambiguous. It is extremely diverse: not only are there thousands of languages in the world, but in each language there is substantial variation in style and genre.
Furthermore, many of the phenomena encountered in language follow long-tail statistical distributions, which makes the production of training data more costly. For these reasons, machine learning architectures for NLP applications tend to be quite different from those used in other fields.