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T1  - Unsupervised zero-shot classification of Finnish documents using pre-trained language models 
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UR  - URN:NBN:fi:hulib-202012155147; http://hdl.handle.net/10138/323019 
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A1  - Leal, Rafael 
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PB  - Helsingin yliopisto 
Y1  - 2020 
LA  - eng 
AB  - In modern Natural Language Processing, document categorisation tasks can achieve success rates of over 95% using fine-tuned neural network models. However, so-called "zero-shot" situations, where specific training data is not available, are researched much less frequently. The objective of this thesis is to investigate how pre-trained Finnish language models fare when classifying documents in a completely unsupervised way: by relying only on their general "knowledge of the world" obtained during... 
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KW  - NLP; space vector model; zero-shot classification; Finnish language; pre-trained language models; Kieliteknologia; Language Technology; Språkteknologi; Kielellisen diversiteetin ja digitaalisten menetelmien maisteriohjelma; Master's Programme Linguistic Diversity in the Digital Age; Magisterprogrammet i språklig diversitet och digitala metoder 
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