Course title in Estonian
Keel ja tehnoloogia
Course title in English
Language and Technology
lecturer of 2022/2023 Autumn semester
Kais Allkivi-Metsoja (language of instruction:Estonian)
lecturer of 2022/2023 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
To support the integration of digital competences and knowledge in a specific field.
a) basic knowledge and practical skills for processing, comparing and visualizing large quantities of textual data;
b) readiness to choose optimal methods and tools for automated text analysis depending on the task, and to explain these preferences;
c) ability to qualitatively interpret the results of quantitative text analysis according to the objective;
d) knowledge on the evolution and applications of text and speech processing.
Brief description of the course
The course introduces natural language processing and software that can be used for automated analysis of Estonian language. It provides an overview of the development of language technology field (focusing on the Estonian context), language resources, and software for processing spoken language (speech synthesis, speech recognition) or written language (including language corpora, digitized literature and archive materials). In the workshops, text mining applications are employed to reveal language use patterns (e.g., n-grams, keywords, collocations, concordances, idioms) that allow to analyze the content of the text. These patterns are interpreted qualitatively, in order to determine the important events, actors, their views and attitudes, drawing conclusions on individual, socio-cultural, political etc. opinions in various texts. Language processing is combined with statistical methods and visualization tools for optimal comparison of large textual data, e.g., observing the discourse of different time periods, genres or authors. Problems concerning the course paper are discussed, e.g., the actuality of topic and objective, forming of research questions, choice of references, methods and tools of analysis.
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 the output of automated text analysis, associating the results with characteristics of sublanguages or individual language use, media events, socio-historic discourse etc.
Study programmes containing that course