Statistical
Natural Language Processing: CSED523
Gary Geunbae Lee, Eng
2-211, gblee@postech.ac.kr, 279-2254 1. Course objectives This course introduces various recent
statistical methods in natural language processing. We will cover basic
statistical tools for computational linguistics and their application to
part-of-speech tagging, statistical parsing, word sense disambiguation,
sentiment analysis, text categorization, machine translation, information
retrieval and statistical language modeling. We also briefly touch
on some topics of statistical language models for speech recognition and
text-to-speech systems, and recent deep learning models for natural language
processing. 2. Course prerequisites no required pre-requisite 3. Grading midterm 40% 4 texts or
references Manning, C. D., Schütze, H.: Foundations of
Statistical Natural Language Processing. The MIT Press. 1999. ISBN 0-262-13360-1.
Jurafsky, D. and J. H. Martin: Speech and
Language Processing. Prentice-Hall. 2009. 2nd edition (3rd edition, 2019
draft: http://web.stanford.edu/~jurafsky/slp3/) Yoav Goldberg. A
Primer on Neural Network Models for Natural Language Processing 5. Others
instruction
language: English 6. Course schedule Statistical inference: n-gram language modeling Markov Models (HMM) / Maximum entropy Deep learning NLP1 / Deep learning NLP2 POS tagging / Probabilistic parsing (PCFG) Semantic Processing / Spoken language understanding Statistical machine translation / Neural machine translation Information extraction/ Application-IR-QA-sum Automatic speech recognition / Text-to-speech |