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732A47 Text Mining

Course information

Course Sections

The three introductory modules are meant to give you the necessary background for the rest of the course.
You need to pass two out of the three introductory modules, and you are free to choose which module (if any) to skip.

Course literature

The following books will be used, in parts, during the course:
  • Natural Language Processing with Python (NLTK).
    This book contains a lot of practical hands-on material using the NLTK toolkit for Python.
    The book's website is here, where the book can be read for free in HTML format. The publisher O'Reilly also sells the book in PDF format.
  • Foundations of Statistical Natural Language Processing (MS).
    This book describes the background theory for computational linguistics and statistical analysis of text data.
    It available electronically for free here (for LiU students, but and probably also for students at most other Swedish universities).
    The book's website is here.
  • Modern Information Retrieval (BYRN) by R Baeza-Yates and B Rebeiro-Neto, Addison-Wesley, 1999.
  • Extra material
Note: We will not order the books to the book stores on campus. Both books are available at all major internet book stores.

Course Introduction

Lecture 1: Course info. General introduction to text mining. Motivating applications.
Teacher: Mattias Villani
Read: MS 1 and NLTK 1-2 | Slides
Code: Intro2NLTK.py

Introduction to Python Programming

Lecture 1: Introduction to the Python programming language.
Teacher: Johan Falkenjack
Read: Chapter 4 in NLTK | Chapters 1-13 in Learning to Program Using Python | Cheat sheet | Slides
Code: PythonL1.py
Other material: Interactive Python web tutorial | Python code visualization | Python tutorial | Codacademy
| Infographic on R vs Python
Lab: Lab

Introduction to Statistical Modeling

Lecture 1: Basic statistics. Regression. Classification.
Teacher: Oleg Sysoev
Read: MS 1 and NLTK 1-2 | Slides
Lab: Lab

Introduction to Computational Linguistics

Lecture 1: Basic linguistics.
Teacher: Marco Kuhlmann
Read: NLTK Chapters 3,5 and 7 | Slides
Lab: Lab

Data models and Information Retrieval for Textual Data

Lecture 1: Basic linguistics.
Teachers: Patrick Lambrix and Zlatan Dragisic
Read: Modern Information Retrieval Chapter 2 (distributed in class) | Slides PDF | Slides Powerpoint
Lab: Lab | Additional instructions

Statistical Models for Textual Data

Teacher: Måns Magnusson

Lecture 1: n-grams. Part-of-speech tagging.
Read: NLTK 5.1-5.2 and MS 2.2 and 6 | Slides

Lecture 2: Document classification.
Read: NLTK 6 | Slides
Code: TM package in R - demo
Other material: Introduction to the tm package in R and slides on using it for classification.

Lecture 3: Topic models.
Read: NLTK Chapters 3,5 and 7 | Article on topic models | Slides
Code: Topic models in R - demo

Lab: Lab. The lab is to be submitted in LISAM, where you also find the submission deadline.

Text Mining Project

Form:The project should be performed and reported individually.
Extent:The project comprises 3 credit points.
Grading: ECTS scale (A-F) for masters students, Pass/Fail for PhD students.
Examination: Written report + Oral presentation.
Deadline for submitting the written report: Jan 17, 2016.

Suggested projects Your are encouraged to select your own topic for the project.
Here is a list with some directions for possible projects.
See also the list below with links to some publically available corpora.

Public corpora
  • UCI Machine learning repository has a collection of text datasets
  • 20 Newsgroups data is a collection of approximately 20,000 newsgroup documents from 20 newsgroups
  • Språkbanken is a collection Swedish corpora from many different sources, ranging from blogs to August Strindberg's personal letters.
  • Google ngrams - text from millions of books scanned by Google.
  • Wikipedia texts can be downloaded. See also the download instructions here. Maybe only for the truly brave ...

Project presentations from previous years

Page responsible: Mattias Villani
Last updated: 2015-11-28