Language and Technology
Course code
old course code
Course title in Estonian
Keel ja tehnoloogia
Course title in English
Language and Technology
ECTS credits
Assessment form
lecturer of 2023/2024 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
lecturer of 2024/2025 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
To develop

- basic knowledge and practical skills for processing, comparing and visualizing large textual data;

- readiness to choose suitable methods and tools for automated text analysis depending on the task, justifying these preferences;

- knowledge on the evolution and applications of text and speech processing.
Brief description of the course
The course introduces the possibilities and applications of natural language processing. It provides an overview of the development of the language technology field as well as various language resources and software for processing spoken and written language (incl. language corpora, digitized literature and archive materials). Data mining is applied to reveal language patterns, which help to highlight the text content, language use preferences, and particularities of a text genre or an author's style. These patterns are interpreted qualitatively, in order to determine the important events, actors, their views and attitudes.

In individual course papers, the students solve a research or applied problem of their interest with the help of language technology.
Learning outcomes in the course
Upon completing the course the student:
- has acquired an overview of the evolution of language technology, electronic language resources and language processing software;
- is able to use automated language analysis knowingly and purposefully in future studies and professional work, defining suitable methods and applications to tackle a specific (cultural, social, linguistic, technological etc.) problem;
- is able to qualitatively interpret and visualize the output of automated text analysis.
Kais Allkivi-Metsoja