MASTER IN LINGUISTICS – COMPUTATIONAL LINGUISTICS

Language(s) of instruction: English

Length of study: 2 years, full time

Course Location: Université de Paris

Degrees awarded: Master Science du langage – Parcours Linguistique informatique

Entry Requirements: Degrees required for registration in first year: 3rd year Bachelor’s degree either:

  • in Language Sciences or Humanities, but with skills in programming (python, java), and basic mathematical training (linear algebra, probability theory);
  • or in computer science, with a strong interest in languages and linguistic formalization (beginners in linguistics may apply).

Admission requirements: The application should contain a copy of the student’s diploma, a CV and a cover letter.

Language pre-requisites:

  • Admission is conditional on a level of English sufficient to read an article and understand a discussion in English. No minimum level of French is required, but basic skills in reading French would definitely help.
  • English: B2+
Course overview

The computational linguistics program enables students to master the techniques of Natural Language Processing (NLP) and their applications. The professional orientation opens up to positions in artificial intelligence companies as computer linguists working on the processing of written texts. The research orientation may allow to pursue a PhD in computational linguistics.

The master’s program consists of two years of two semesters each.

  • The first year focuses on the formal foundations of NLP, machine learning and the articulation with the formal description of languages. The courses are complemented by practical work on computers.
  • In the second year, the first semester includes in-depth courses, in particular for the research track, and application courses for the industrial track.  The second semester is a research internship in a laboratory or an internship in a company, with the writing of a dissertation and its defense.
Skills and competencies developed
  • Know how to explore and computerize large and varied corpora of texts;
  • Know state-of-the art algorithms for Natural Language Processing and Machine Learning and how to use them to solve a given task ;
  • Be able to use deep learning libraries ;
  • Know how to design and implement new algorithms to solve specific tasks ;
  • Know modern linguistic concepts allowing the description of various languages.