Introduction to automatic text processing

lfial2630  2019-2020  Louvain-la-Neuve

Introduction to automatic text processing
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
22.5 h
Q2
Teacher(s)
Language
French
Prerequisites
There are no prerequisites, but this course requires at least an interest in computer science and a familiarity with computers.
Main themes
Human science specialists are more and more often faced with situations where they have to work with large amounts of textual data (literary, historical or political texts, linguistic research data, etc.). Computer tools offer undeniable advantages for the analysis, organisation, sorting or formatting of this information. However, it is necessary to be able to master these tools and select an appropriate method.
The aim of this course is to initiate students into programming and algorithmics through a programming language that is particularly suitable for text processing: Perl. Students will learn to solve increasingly complex problems and build computer programs that can analyse textual data.
Students with experience in programming (NLP, computer data science, etc.) are not exempt from the course, but will receive more advanced exercises and specific support.
Aims

At the end of this learning unit, the student is able to :

1

By the end of the course, students will be able to analyse a problem related to textual data processing and to design and build a computer program to address this problem. To do this, they will have gained a basic knowledge of algorithmics and programming and will be able to apply it on their own.

Students will also acquire more informed and more critical insight into how human science software works.

 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
Classes are divided between lectures presenting the tools and methods, and tutorials aiming to allow students to experiment with methods and software.
Teaching methods
Lectures; exercises completed during the course and in the form of home assignments.
Evaluation methods
  • Continuous assessment during term-time, based on participation in exercises (30% of the final grade);
  • Final programming project documented in a report and presented during an oral exam, plus completion of one or more programming exercises during this oral exam (70% of the final grade).
Online resources
Course slides and supplementary, marked exercises are available on the Moodle platform.
Faculty or entity


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Ethics

Master [60] in History of Art and Archaeology: Musicology

Master [120] in Translation

Master [120] in French and Romance Languages and Literatures : French as a Foreign Language

Master [60] in History of Art and Archaeology : General

Master [120] in Ancient and Modern Languages and Literatures

Master [60] in History

Master [120] in Information and Communication Science and Technology

Master [120] in Multilingual Communication

Master [120] in Interpreting

Master [120] in History of Art and Archaeology : General

Master [120] in Linguistics

Master [120] in History of Art and Archaeology: Musicology

Master [120] in Philosophy

Master [120] in History

Master [120] in Data Science : Statistic