Language Technology teaching

Language Technology study module (25 credits)

Organized together with the Department of Future Technologies and the School of Languages and Translation Studies. No prior knowledge of language technology is needed. Students coming outside the Department of IT will learn the basics of programming and automatic text processing during the first and second period courses so that they are able to continue to more advanced cources. All advanced courses are organized so that motivated students also outside IT Department are able to complete the study module.

Courses

KKLT0030 Automaattinen tekstiprosessointi (4 op)

Teacher: Veronika Laippala, School of Languages and Translation studies

Language: Finnish

Time: Every year, first period

Level: Intermediate

Basic Unix commands for automatic text processing, such as reading files and directories, counting and sorting words, searching and replacing with regular expressions, processing text files using piped commands and bash scripts, processing a list of files in a for loop.

BIOI2250 Introduction to Programming (5–6 ECTS)

Teacher: Department of Future Technologies

Language: English

Time: Every year, first and second period

Level: Intermediate

Basics of programming for students with no prior experience. The course uses Python programming language. Students from the Department of Future Technologies (TKT or DI) cannot take this course, you must take the programming courses meant for TKT or DI degree students.

TKO_2091 Johdatus luonnollisen kielen käsittelyyn (5 op) (aka. Introduction to Language Technology)

Teacher: Jenna Kanerva and Kai Hakala, Department of Future Technologies

Language: Finnish

Time: Every year, autumn

Level: Intermediate

The course introduces the basic concepts and applications of language technology, such as morphological tagging, machine translation and sentiment analysis, introduces interesting problems which make human language difficult for computer to understand, how language can be modeled in a machine learnable way, and teaches of how to apply machine learning and neural networks in language technology applications. Machine learning is covered from a very practical point of view and the course includes the very basics of machine learning theory needed, so no prior knowledge is needed.

KKLT0031 Kieliteknologian sovellukset ja mahdollisuudet (5 op)

Teacher: Veronika Laippala, School of Languages and Translation studies

Language: Finnish

Time: Every year, spring

Level: Advanced

The course introduces the basic applications of language technology from the perspective of linguistics and digital humanities.

TKO_2099 Text Mining (5 op)

Teacher: Filip Ginter, Department of Future Technologies

Language: English

Time: Every third year, spring

Level: Advanced

Understanding of the fundamental methods of mining information hidden in very large collections of text and integrating the result with other available databases and resources. The course will specifically focus on text mining of scientific literature in the biomedical domain, but will also foray into other domains. The students will gain a hands-on experience with the relevant tools and resources and will be able to apply the techniques to new domains.

TKO_2098 Information Retrieval (5 op) (aka. Search Engines)

Teacher: Filip Ginter, Department of Future Technologies

Language: English

Time: Every third year, spring

Level: Advanced

Understanding of the fundamental methods of document and information retrieval, ranging from basic document indexing to vector space representations, document classification, clustering and topic modeling. Special attention is given to dealing with large document collections and web search, as well as work with existing tools and libraries for text indexing and retrieval. The course content can include for example: Index construction, vector space model, probabilistic retrieval, document classification, document clustering, link analysis, retrieval in morphologically rich languages, cross-language retrieval, evaluation

TKO_2101 Natural Language Processing (5 op) (aka. Neural Networks in Language Technology)

Teacher: Filip Ginter, Department of Future Technologies

Language: English

Time: Every third year, spring

Level: Advanced

The course focuses on the fundamental methods of natural language processing. The students learn how to apply machine learning methods to deal with the ambiguity present in human language at all levels. Working their way from elementary n-gram models to methods of natural language understanding, the students will learn the relevant methods and how to apply them to real-world data, using existing software packages and libraries. The course content can include for example: Language models, morphological and syntactic analysis, computational semantics, deep learning and vector space representations, large-scale data resources, evaluation