Statistical Natural Language Processing

This course teaches students the major skills needed to tackle typical natural language processing (NLP) tasks with statistical methods. Starting from basics of NLP and machine learning, the course introduces the main learning-based NLP techniques, from clustering and classification to sequence labeling and neural language models. The application of these techniques is exemplified for various NLP tasks, such as topic modeling, sentiment analysis, and coreference resolution. Students learn to design, implement, and evaluate respective NLP methods, both theoretically and in practical assignments.

• Basics of Data Science
• Basics of Natural Language Processing
• NLP using Similarities and Clustering
• NLP using Classification and Regression
• NLP using Sequence Labeling
• NLP using Neural Networks
• NLP using Transfomers
• Practical Issues

• Basics of statistics
• Knowledge of programming, ideally Python
• Any course on machine learning or artificial intelligence
• Bachelor's course: Introduction to Natural Language Processing

• Daniel Jurafsky and James H. Martin. 2009. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. Prentice-Hall, 2nd edition. 
• Free draft of third edition: Speech and Language Processing

• Part 0 – Organizational information [slides]
• Part 1 – Overview [slides]
• Part 2 – Basics of Data Science [slides]
• Part 3 – Basics of Natural Language Processing [slides]
• Part 4 – NLP using Similarities and Clustering [slides]
• Part 5 – NLP using Classification and Regression [slides]
• Part 6 – NLP using Sequence Labeling [slides]
• Part 7 – NLP using Neural Networks [slides]
• Part 8 – NLP using Transformers [slides]