Computational Linguistics

linfo2263  2021-2022  Louvain-la-Neuve

Computational Linguistics
5 credits
30.0 h + 15.0 h
Q1
Teacher(s)
Language
English
Main themes
  • Various levels of linguistic analysis
  • Corpus processing
  • Part-of-speech tagging
  • Probabilistic language modeling (N-grams and Hidden Markov Models)
  • Formal grammars and parsing algorithms
  • Machine translation, deep learning 
  • Linguistics engineering applications such as automatic completion software, POS tagging, parsing or machine translation
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, in particular large scale corpora, possibly annotated or structured
  • apply in a relevant way statistical language modeling techniques
  • implement recent machine learning methods applied to language processing
  • develop linguistic engineering applications
Students will have developed skills and operational methodology. In particular, they have developed their ability to
  • integrate a multidisciplinary approach between computer science and linguistics, using wisely the terminology, tools and existing methods,
  • manage the time available to complete projects of medium size,
  • manipulate and exploit large amounts of data.
 
Content
  • Various levels of linguistic analysis
  • (Automated) corpus processing: formating, tokenization, data tagging
  • Probabilistic language models: N-grams, HMMs
  • Part-of-Speech Tagging
  • (Probabilistic) Context-Free Grammars: parameter estimation and parsing algorithms
  • Introduction to Machine Translation
  • Introduction to Deep Learning
  • Typical linguistic applications such as automated completion, POS taggers, parsing or machine translation.
Teaching methods
  • Lectures
  • Practical projects implemented in Python on the Inginious platform.
Evaluation methods
The projects are worth 30 % of the final grade, 70 % for the final exam (closed-book).
The projects cannot be implemented again in second session.
The project grades are fixed at the end of the semester and included as such in the global score for the second session.
The final exam is, by default, a written exam (on paper or, when appropriate, on a computer).
Bibliography
One recommended textbook - un ouvrage conseillé :
  • Speech and Language Processing, D. Jurafsky and J.H. Martin, Prentice Hall.
Teaching materials
  • Les supports obligatoires sont constitués de l'ensemble des documents (transparents des cours magistraux, énoncés des travaux pratiques, compléments, ...) disponibles depuis le site Moodle du cours.
  • Required teaching material include all documents (lecture slides, project assignments, complements, ...) available from the Moodle website for this course.
Faculty or entity


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

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

Master [120] in Data Science Engineering

Master [120] in Computer Science and Engineering

Master [120] in Data Science: Information Technology

Master [120] in Computer Science

Master [120] in Data Science : Statistic