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Text Mining

2016HT

Status Running - no longer open for registrations
School Computer and Information Science (CIS)
Division STIMA
Owner Mattias Villani
Homepage http://www.ida.liu.se/edu/ugrad/course.sv/732A92

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Course plan

No of lectures

2-3 preparatory lectures (Python, Linguistics, Statistics) + 2 lectures on information retrieval + 3 lectures statistical methods for text mining.

Recommended for

PhD students in Statistics, Computer Science and Cognitive Sciences.

The course was last given

Fall 2015

Goals

The course aims to show how to textual data can be retrieved, linguistically pre-processed and subsequently analyzed quantitatively using formal statistical methods and models. The course brings together expertise from the areas of database methodology, computational linguistics and statistics.

Prerequisites

Organization

The course consists of lectures, lab exercises and a text mining project. The lectures are devoted to presentations of concepts, and methods. The computer exercises are devoted to practical application of text mining tools. In the project work, the student will get hands-on experience in solving a text mining problem.
Language of instruction: English.

Contents

The course aims to show how to textual data can be retrieved, linguistically pre-processed and subsequently analyzed quantitatively using formal statistical methods and models. The course brings together expertise from the areas of database methodology, computational linguistics and statistics.
The course proceeds in four stages:
* Introductory modules
- Introduction to Python programming
- Introduction to statistical modeling
- Introduction to computational linguistics
* Data models and information retrieval for textual data
* Statistical models for textual data
* Text mining project

Literature

http://www.ida.liu.se/~732A92/info/courseinfo.en.shtml

Lecturers

Mattias Villani
Oleg Sysoev
Marco Kuhlmann
Patrick Lambrix

Examiner

Mattias Villani

Examination

Text mining project report. Written reports on lab assignments.

Credit

6 ECTS

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