Coursebooks 2017-2018


Human language technology: applications to information access


Lecturer(s) :

Popescu-Belis Andrei




Every 2 years


Every 2 years. Next time: Fall 2018


The HLT course introduces applications of human language technology focusing on accessing text information across three types of barriers: the quantity barrier (large repositories), the cross-lingual barrier, and the subjective barrier (human interactions).


The following technologies will be studied for each barrier to information access:

1. The quantity barrier: information retrieval, web search, document classification, topic models, learning to rank, question answering, recommender systems.
2. The crosslingual barrier: machine translation (history of the field, presentation of rule-based and of statistical systems including phrase-based and tree-based ones, domain adaptation, the use of syntax and semantics), methods for text alignment, issues and metrics for MT evaluation, cross-language information retrieval.
3. The subjective barrier: sentiment analysis, subjectivity detection, analysis of human exchanges (spoken or written) for information access, search within multimedia archives.
4. Conclusion on the bases of HLT research: defining a problem, building reference data, finding features for machine learning algorithms, training the algorithms, evaluating and analyzing the performance.



The course includes lectures (2h) followed by laboratory exercises (2h) using freely-available software and language resources to perform one of the tasks introduced in the course and to illustrate the properties of one or several presented algorithms. The exercises will serve as starting points for the individual projects (graded based on report and oral defense at the end of the semester), on a topic to be chosen in agreement with the lecturer. Once in the semester students will present a scientific article, and one laboratory exercise will be graded.


Human language technology, language engineering, information retrieval, machine translation.

Learning Prerequisites

Recommended courses

At least one prior course in statistics, machine learning, computational linguistics, or artificial intelligence. Programming proficiency in a language such as Perl or Java.

Assessment methods

Project report.
Oral presentation.



In the programs

Reference week

      Exercise, TP
      Project, other


  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German