This is a graduate course in computer science, studying topics in natural Language processing. This course covers natural language processing tasks such as part-of-speech tagging, dependency parsing, named entity recognition, coreference resolution, and sentiment analysis as well as machine learning algorithms such as stochastic gradient descent, structured prediction, softmax regression, and neural networks. Advanced topics such as abstract meaning representation, word embeddings, and deep learning are also discussed. Prerequisites: undergraduate level of artificial intelligence, machine learning, natural language processing, information retrieval, or their equivalence.

• Syllabus.
• Schedule.
• Reading.
• Quizzes.

• How to install.
• How to decode.

• Part-of-speech tagging.

• Create an NLP component.

• Configuration format.
• Part-of-speech tags.