Computational Linguistics

LINGI2263  2018-2019  Louvain-la-Neuve

Computational Linguistics
5.0 credits
30.0 h + 15.0 h

Fairon Cédrick ; Dupont Pierre ;
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

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”.

  • 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


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

Faculty or entity

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

Program title
Master [120] in Computer Science and Engineering

Master [120] in data Science: Statistic

Master [120] in Linguistics

Master [120] in Data Science Engineering

Master [120] in data Science: Information technology

Master [120] in Computer Science