2021 Spring/Fall Changes
The Natural Language Processing A/B courses are replaced with corresponding Digital Humanities (Language Processing and Information Retrieval) courses. A new website will be up by 2021/4/12.
Course Introduction
Class materials consist of reading materials, videos, and JupyterHub notebooks.
Introduction to Natural Language Processing (NLP A)
This introductory course provides an overview of the field of Natural Language Processing (NLP). It is primarily aimed at students from humanities backgrounds, with the end goal being to expose students to the possibilities and current limitations of the field, especially in connection with the varied backgrounds and research goals of each participating student.
Classes consist of a combination of reading assignments and in-class discussions of several topics, including part-of-speech tagging, morphological analysis, and syntactic and semantic parsing, and programming exercises showcasing their practical use in tasks ranging from text classification and author attribution to word sense disambiguation and word embeddings. An emphasis will also be put on gaining proficiency with standard NLP tools for English and Japanese.
Go to the official NLP A page for more information.
Applied Natural Language Processing (NLP B) Topics
Applied Natural Language Processing is a continuation of the Introduction to Natural Language Processing course focused on both deepening students’ knowledge in select topics as well as providing them the opportunity to work on their own research project. The first part of the class will feature readings and programming assignments that culminate in showcasing an NLP system made from multiple components. The second part of the class will be used for individual consultations and, finally, students’ final topic presentations.
Go to the official NLP B page for more information.
External Resources
Books on NLP:
- Japanese
- English
Online resources:
- 首都大学東京 小町研の自然言語処理を独習したい人のために と 自然言語処理を学ぶ推薦書籍
- Graham Neubig (Carnegie Mellon University): Graham Neubig’s Teaching
There are a multitude of MOOC’s and online courses available for free online that cover various aspects of NLP, Statistics, and ML. The following links should provide further opportunities to learn for those interested:
- MOOCs (Coursera)
- Jurafsky’s and Manning’s Natural Language Processing on Coursera (Stanford University)
- Radev’s Introduction to Natural Language Processing on Coursera (University of Michigan)
- Collins’ Natural Language Processing on Coursera (Columbia University)
- [Machine Learning] Ng’s Machine Learning on Coursera (Stanford University)
- CS224n: Natural Language Processing with Deep Learning (Stanford University)
Schedule
Time and Place
| Course Title | Term | Semester | Day | Period | Classroom |
|---|---|---|---|---|---|
| Natural Language Processing A | I | Spring | Tuesday | 3 | B307/A606 |
| Natural Language Processing B | II | Fall | Tuesday | 3 | A606/B307 |
Classes are held in the A606 room (6th floor) inside the Graduate School of Language and Culture building on Toyonaka campus, but will be held in B307 from November 2019. The classroom is equipped with iMacs, so bringing your own computer is optional, but recommended. Additionally, a pre-set programming environment (Jupyter) with various English- and Japanese-language NLP tools is already provided. For more information, see the infrastructure page.
Coursework (Jupyter)
JupyterHub
Please access the Jupyter interactive programming environment from here (Note: the site is password protected–make sure to attend the first class to receive your designated username and password).