Statistical Natural Language Processing (SNLP)

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%

final 40%

home works 20%

 

4  Texts or References

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.

 

5. Others

instruction language: English

homework will be on solving NLP application problems including Python programming

 

6. Course schedule



YNLP_Online

Ynlp_1stday (9hrs)

Ynlp_2ndday

Ynlp_3rdday

AI&DS _online

         ai&ds slide (3hrs)

chatgpt_online

        chatgpt slide