CS 6840: Natural Language Processing
Time and Location: Tue, Thu 9:00 – 10:20am, ARC 101
Instructor: Razvan Bunescu
Office: Stocker 341
Office Hours: Tue, Thu 10:30 – 11:30am, or by email appointment
Email: bunescu @ ohio edu
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, by Daniel Juraksfy and James E. Martin. McGraw Hill, 2008 (2nd edition).
Recommended Supplementary Text:
Foundations of Statistical Natural Language Processing by Christopher Manning and Hinrich Schütze. MIT Press, 1999.
Natural Language Processing (NLP) is a branch of Artificial Intelligence concerned with developing computer systems that can process, understand, or generate natural language. Major applications of NLP include information retrieval and web search engines, information extraction, question answering, machine translation, sentiment analysis, text mining, and speech recognition. This graduate level course will give a fairly broad overview of NLP, with a primary focus on tasks that are widely seen as fundamental for a natural language understanding system such as part of speech tagging, syntactic parsing, word sense disambiguation, semantic role labeling, coreference resolution, and semantic parsing.
Students are expected to have basic knowledge of formal languages (regular and context free grammars) and to be comfortable with programming. Relevant background material in elementary linear algebra, probability theory and information theory will be made available during the course. Knowledge of machine learning and linguistics will be very useful, though not strictly necessary.
- Syllabus & Introduction
- Language Models
- Part of Speech Tagging
- Lexical Semantics and Word Sense Disambiguation
- Hidden Markov Models
- Syntax and Grammars
- Syntactic Parsing
- Statistical Parsing
- Conditional Random Fields