Human Language Technology:
Applications to Information Access
This course introduces recent applications of human language technology (HLT), presenting the basic knowledge required to implement them, with an overview of possible alternatives, evaluation methods (including notions about evaluation campaigns), and challenges or limits of the state of the art. The technologies focus on the problem of accessing text-based information across three main types of barriers: the quantity barrier (accessing information in very large repositories), the crosslingual barrier (accessing information across languages through machine translation), and the subjective barrier (accessing information that is enclosed in complex human interactions). The following technologies will be studied for each barrier to information access.
The course includes lectures (2h) followed by laboratory exercises (2h) using freely-available software and language resources (on each student's personal computer) to perform some 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 in January 2017), 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.
Keywords: human language technology, language engineering, information retrieval, machine translation.
Required prior knowledge: at least one prior course in statistics, machine learning, computational linguistics, or artificial intelligence. Programming proficiency in a language such as Perl or Java.
Form of examination: project report with oral presentation in the exam session of January 2017 (in addition, one paper presentation and one practical work will contribute to the grade)