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,
but mathematics (probability, statistics, linear algebra, etc), machine learning, python programming knowledge will be helpful to follow the course.

midterm 35%
final 35%
home works 30%

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
POS tagging / Probabilistic parsing (PCFG) / Semantic Processing
Deep learning word2vec / Deep learning neural text classification
Statistical machine translation / Neural machine translation
Information extraction/ Application-IR-QA-sum
Automatic speech recognition / Text-to-speech
Spoken language understanding / Dialog management
Deep learning NLP application architecture

instruction language: English
2 homeworks will be on solving NLP application problems including Python programming
course home page:
https://nlp.postech.ac.kr/courses/statistical-natural-language-processing