Computational Linguistics

lingi2263  2018-2019  Louvain-la-Neuve

Computational Linguistics
5 credits
30.0 h + 15.0 h
Q1
Teacher(s)
Dupont Pierre; Fairon Cédrick;
Language
English
Main themes
  • Basics in phonology, morphology, syntax and semantics
  • Linguistic resources
  • Part-of-speech tagging
  • Statistical language modeling (N-grams and Hidden Markov Models)
  • Robust parsing techniques, probabilistic context-free grammars
  • Linguistics engineering applications such as spell or syntax checking software, POS tagging, document indexing and retrieval, text categorization
Aims

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

1

Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

  • INFO1.1-3
  • INFO2.3-4
  • INFO5.3-5
  • INFO6.1, INFO6.4

Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

  • SINF1.M4
  • SINF2.3-4
  • SINF5.3-5
  • SINF6.1, SINF6.4

Students completing successfully this course should be able to

  • describe the fundamental concepts of natural language modeling
  • master the methodology of using linguistic resources (corpora, dictionaries, semantic networks, etc) and make an argued choice between various linguistic resources
  • apply in a relevant way statistical language modeling techniques
  • develop linguistic engineering applications

Students will have developed skills and operational methodology. In particular, they have developed their ability to

  • integrate a multidisciplinary approach to the edge between computer science and linguistics, using wisely the terminology and tools of one or the other discipline,
  • manage the time available to complete mini-projects,
  • manipulate and exploit large amounts of data.
 

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
  • Linguistic essentials: morphology, part-of-speech, phrase structure, semantics and pragmatics
  • Mathematical foundations: formal languages, and elements of information theory
  • Corpus analysis: formating, tokenization, morphology, data tagging
  • N-grams: maximum likelihood estimation and smoothing
  • Hidden Markov Models: definitions, Baum-Welch and Viterbi algorithms
  • Part-of-Speech Tagging
  • Probabilistic Context-Free Grammars: parameter estimation and parsing algorithms, tree banks
  • Machine Translation: classical and statistical methods (IBM models, Phrase-based models), evaluation
  • Applications: SMS predictors, POS taggers, information extraction
    http://moodleucl.uclouvain.be/course/view.php?id=7865
Teaching methods
  • 12 lectures
  • 3 miniprojects
  • feedback sessions about the miniprojects
Evaluation methods
25% for practical works + 75% final exam (closed book)

No possibility to present again practical works in the second session
Online resources
http://moodleucl.uclouvain.be/course/view.php?id=7865
Bibliography
Slides obligatoires disponibles sur le site :
http://moodleucl.uclouvain.be/course/view.php?id=7865 1 textbook conseillé :
Faculty or entity
INFO


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

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering
DATE2M
5
Master [120] in Computer Science and Engineering
INFO2M
5
Master [120] in Linguistics
LING2M
5
Master [120] in Computer Science
SINF2M
5
Master [120] in data Science: Statistic
DATS2M
5
Master [120] in data Science: Information technology
DATI2M
5