Introduction to Natural Language Processing

CS 585, Fall 2017, UMass Amherst CS
Lecture: TTh 2:30-3:45, Thompson 106

Instructor: Brendan O'Connor
TAs: Satya Narayan Shukla, Abe Handler
Email (to all of us): cs585-instructors -at- googlegroups.com
Links:

• Schedule
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• Piazza page (discussion, announcements, non-public materials)
• Gradescope page (assignment submission)
• Moodle (for code submission and lecture recordings)

Course description

Natural Language Processing (NLP) is the engineering art and science of how to teach computers to understand human language. NLP is a type of artificial intelligence technology, and it's now ubiquitous -- NLP lets us talk to our phones, use the web to answer questions, map out discussions in books and social media, and even translate between human languages. Since language is rich, ambiguous, and very difficult for computers to understand, these systems can sometimes seem like magic -- but these are engineering problems we can tackle with data, math, and insights from linguistics.

This course will introduce NLP methods and applications including probabilistic language models, machine translation, and parsing algorithms for syntax and the deeper meaning of text. During the course, students will (1) learn and derive mathematical models and algorithms for NLP; (2) become familiar with key facts about human language that motivate them, and help practitioners know what problems are possible to solve; and (3) complete a series of hands-on projects to implement, experiment with, and improve NLP models, gaining practical skills for natural language systems engineering.

This course is intended for upper-level CS undergraduates and masters-level graduate students, as well as linguistics students with an appropriate background.

Prerequisites: experience in programming and probability. Undergraduates must have completed:
  ((CS220 or CS230) and CS240) or Ling492B)
This is intended to represent the following requirements:

1. Programming maturity, including data structures and recursion (e.g. comfort with implementing and debugging depth-first search).
2. Basic algorithm analysis (e.g. big-O analysis of a graph algorithms).
3. Basic probability theory (e.g. Bayes Rule).
4. A genuine interest in language; linguistics background is a huge plus.

Related courses

See also previous offerings of this course.

If you are interested in other courses, see this list of courses in NLP and related areas offered at UMass and nearby.

Readings

The suggested textbook is Jurafsky and Martin, Speech and Language Processing, 2nd ed. We will also draw readings from J+M's 3rd edition textbook. (We will post all readings as PDFs.)

Other useful texts for NLP include:

• Manning and Schütze, Foundations of Stat NLP. Free access at UMass.
• Eisenstein, Natural Language Processing. Draft textbook.
• Smith, Linguistic Structure Prediction. Free access at UMass. Short book. Excellent coverage of structured prediction inference methods for NLP.
• Murphy, Machine Learning: a Probabilistic Perspective. Excellent, though advanced, coverage of most of the machine learning methods we will use.
• Bender, Linguistic Fundamentals for NLP. Short book. Focuses on linguisic issues relevant to NLP.
• Bird et al, NLP with Python, a.k.a. the NLTK book. Aimed at a more introductory level than this course, but the book is a good gentle introduction to NLP with a CL (computational linguistics) emphasis. The NLTK software has easy-to-use data access and some interfaces to (not always SOTA) NLP tools.

Courses elsewehre