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CSCI 5832: Natural Language Processing

Instructor Fall 2019: James Martin Natural Language Processing

NLP is about getting computers to perform useful and interesting tasks involving spoken and written human language. NLP is sometimes referred to as Computational Linguistics to emphasize the fact that involves the combination of CS methods with research insights from Linguistics (the study of human language). Practical applications of NLP include question answering, machine translation, information extraction, and interactive dialog systems (both written and spoken). Modern NLP systems rely heavily on methods involving probability, linear algebra and calculus often in combination with machine learning methods.

Course Topics (Subject to Change)

Words, word counting, and lexicons

Probabilistic language modeling

Text classification with language models

Text classification with single layer neural networks

Vector semantics (word embeddings)

Part-of-speech tagging with Hidden Markov Models

Dependency parsing

Compositional Semantic Analysis

Deep learning models

Recurrent networks

Transformer networks

Information extraction

Question answering

Machine Translation

Readings

We'll be using draft chapters from the 3rd Edition of Speech and Language Processing by Dan Jurafsky and James H. Martin. You should not need to buy the current edition, draft pdfs of the new chapters will be available from the textbook website. The week-to-week readings are on Moodle

Grading

Grades will be based on a cumulative score as follows:● Assignments (40%)● Quizzes (30%; equally weighted)● Final (30%)

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