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 thousandsof languages in the world, but in each language there is substantial variation in style and genre.<br> Furthermore, many of the phenomena encountered in language follow long-tailstatistical 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. The course covers the following broad areas: - Working practically with text data, including fundamental tasks such as tokenization and word counting. - Probabilistic models for text, such as topic models. - Overview of the most common types of NLP applications - Architectures for representation in NLP models, including word embeddings, convolutional and recurrent neural network, and attention models. - Machine learning models for common types of NLP problems, mainlycategorization, sequence labeling, structured prediction and generation. - Approaches to transfer learning in NLP.