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.

no required pre-requisite

midterm 40%
final 40%
home works 20%

Jacob Eisenstein. Natural Language Processing (2018, draft)
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
Manning, C. D., Schütze, H.: Foundations of Statistical Natural Language Processing. The MIT Press. 1999. ISBN 0-262-13360-1.

Introduction
Mathematical foundation
Linguistic essentials
Text processing-Collocations
Statistical inference: n-gram language modeling
TC-WSD-Sentiment
Markov Models (HMM) / Maximum entropy
Deep learning NLP1 / Deep learning NLP2
POS tagging / Probabilistic parsing (PCFG) / Semantic Processing
Statistical machine translation / Neural machine translation
Information extraction/ Application-IR-QA-sum
Automatic speech recognition / Text-to-speech
Spoken language understanding / Dialog management

lecture and 2 homeworks
course home page:
http://isoft.postech.ac.kr/Course/CS730b/2005/index.html
*first two weeks online using zoom icon in plms, and the rest of the courses off-line; if covit-19 patients occurs in the class, switch to online for the next two classes.

instruction language: English
homework will be on solving NLP application problems including Python programming