TurkuNLP organizes several courses related to language technology and digital language studies, both at the Department of Computing and School of Languages and Translation studies. See the list of courses below.

Language Technology study module (25 credits)

The courses can be combined into Digital Language Studies Minor Subject (25-40 ects). More information available at opas.peppi.utu.fi. The minor subject studies are open for all University of Turku students, and no prior knowledge of programming, machine learning or language research is required. However, note that if continuing to the advanced computer sciences courses (not compulsory for the minor subject studies), fluency in Python is expected.

Courses at the Department of Computing, University of Turku

Persons in charge: Filip Ginter, Sampo Pyysalo, Jenna Kanerva, Department of Computing

For up-to-date teaching schedules, see opas.peppi.utu.fi (search with course code or name).

TKO_7095 Introduction to Human Language Technology (5 ects)

Language: English

Level: Advanced Studies

After completing the course, the student will be able to • Explain several different language technology applications, • Analyze the most important characteristics of human language as a data source, • Select or create a suitable annotated text corpus for the given task, • Discuss the basic feature representations of language data in machine learning applications, • Implement a simple machine learning pipeline for the given language technology application, and • Explain the idea behind semantic vector spaces and summarize the main methods used to learn meaningful vector spaces.

TKO_8965 Deep Learning in Human Language Technology (5 ects)

Language: English

Level: Advanced Studies

After completing the course, the student will be able to • understand basic and advanced deep neural network architectures and their application to various tasks in natural language processing, • select appropriate language resources and deep learning models and fine-tune state-of-the-art models for a range of tasks involving natural language, • understand and explain the capabilities and limitations of deep learning-based models and concepts such as transfer learning, multi- and cross-lingual models, and large-scale pre-training, • independently implement multi-stage natural language processing systems combining several task-specific models.

TKO_8964 Textual Data Analysis (5 ects)

Language: English

Level: Advanced Studies

The student will • apply and further expand the understanding of deep neural network models gained in the Deep Learning in Human Language Technology course, • recognize and understand the most important text analysis tasks typically faced in research and data science industry, • understand what methods and datasets apply to these tasks, and their limitations, • be able to gather relevant data and critically assess its quality as well as the quality of the method output, and • be able to independently implement basic text analysis tasks using modern neural network models and Python libraries.

Courses at the Digital Language Studies, University of Turku

Person in charge: Veronika Laippala, Digital Language Studies

For up-to-date teaching schedules, see opas.peppi.utu.fi (search with course code or name).

KKLT0040 Corpus Linguistics and Language Technology (5 ects)

Language: Finnish, English

Level: Intermediate Studies

After the course the student is familiar with ready-made corpora from different fields, understands the importance of corpora in linguistics and knows how to avoid the most common problems in corpus compilation. Further, the student knows how to use corpus tools, such as Antconc and Wordsmith, is familiar with basic natural language processing tools and their functioning and understands the potentials of machine learning for language studies.

KKLT0030 Automatic Text Processing (5 ects)

Language: Finnish, English

Level: Advanced Studies

After the course the student knows how to manipulate and analyze large corpora from command line. The student is familiar with various simple Unix tools, such as counting frequencies, using regular expressions and running loops. Further, the student knows how to search for instructions in online manuals. The practical assignments prepare the students to apply the learned skills for instance in theses, and the learned skills are further developed in the more advanced courses of the studying module.

DIKI1002 Working with Text in Python (5 ects)

Language: Finnish, English

Level: Advanced Studies

After the course, the students know: • how computers process and store text, • how to load text into Python for processing and save the results, • how to do basic tasks in natural language processing and the kinds of annotations they produce, • how to evaluate the performance of natural language processing algorithms, and • how to store texts and prepare them for further analysis.

DIKI1007 Natural Language Processing for Humanities (5 ects)

Language: Finnish, English

Level: Advanced Studies

After the course, the students know how to • create linguistic annotations for diverse languages • search linguistic annotations for structural patterns • quantify linguistic information • understand the distributional hypothesis • understand the basics of word embeddings, and • know how to work with common annotation schemas.

DIKI1001 Hands-On Project on Digital Language Studies (5 ects)

Language: Finnish, English

Level: Advanced Studies

After the course, the students will be able to • understand the scope of quantitative methods and how they benefit linguistics • plan a research project in the domain of digital language studies by applying digital methods to research questions that rise from linguistics • carry out a project to a conclusion within a time frame • report/document on the project findings.