Sirvi Autor "Laippala, Veronika" järgi

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Building a Large Automatically Parsed Corpus of Finnish
(Oslo, Norway, Linköping University Electronic Press, Sweden, pp. 291--300, 2013) Ginter, Filip; Nyblom, Jenna; Laippala, Veronika; Kohonen, Samuel; Haverinen, Katri; Vihjanen, Simo; Salakoski, Tapio; Oepen, Stephan; Hagen, Kristin; Johannessen, Janne Bondi
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Creating register sub-corpora for the Finnish Internet Parsebank
(Gothenburg, Sweden, Association for Computational Linguistics, pp. 152--161, 2017) Laippala, Veronika; Luotolahti, Juhani; Kyröläinen, Aki-Juhani; Salakoski, Tapio; Ginter, Filip; Tiedemann, Jörg; Tahmasebi, Nina
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Multilingual and Zero-Shot is Closing in on Monolingual Web Register Classification
(Reykjavik, Iceland (Online), Linköping University Electronic Press, Sweden, pp. 157--165, 2021) Rönnqvist, Samuel; Skantsi, Valtteri; Oinonen, Miika; Laippala, Veronika; Dobnik, Simon; Øvrelid, Lilja
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Parsing Clinical Finnish: Experiments with Rule-Based and Statistical Dependency Parsers
(Odense, Denmark, Northern European Association for Language Technology (NEALT), pp. 65--72, 2009) Haverinen, Katri; Ginter, Filip; Laippala, Veronika; Salakoski, Tapio; Jokinen, Kristiina; Bick, Eckhard
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Parsing Clinical Finnish: Experiments with Rule-Based and Statistical Dependency Parsers
(2009-05-13T11:07:10Z) Haverinen, Katri; Ginter, Filip; Laippala, Veronika; Salakoski, Tapio
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Perspectives on Forests and Forestry in Finnish Online Discussions - A Topic Modeling Approach to Suomi24
(University of Tartu Library, 2025-03) Peura, Telma; Krizsán, Attila; Kuusalu, Salla-Riikka; Laippala, Veronika; Basile, Valerio; Bosco, Cristina; Grasso, Francesca; Ibrahim, Muhammad Okky; Skeppstedt, Maria; Stede, Manfred
This paper explores how forests and forest industry are perceived on the largest online discussion forum in Finland, Suomi24 ('Finland24'). Using 30,636 posts published in 2014–2020, we investigate what kind of topics and perspectives towards forest management can be found. We use BERTopic as our topic modeling approach and evaluate the results of its different modular combinations. As the dataset is not labeled, we demonstrate the validity of our best model through illustrating some of the topics about forest use. The results show that a combination of UMAP and K-means leads to the best topic quality. Our exploratory qualitative analysis indicates that the posts reflect polarized discourses between the forest industry and forest conservation adherents.
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Toward Multilingual Identification of Online Registers
(Turku, Finland, Linköping University Electronic Press, pp. 292--297, 2019) Laippala, Veronika; Kyllönen, Roosa; Egbert, Jesse; Biber, Douglas; Pyysalo, Sampo; Hartmann, Mareike; Plank, Barbara
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Towards a Dependency-Based PropBank of General Finnish
(Oslo, Norway, Linköping University Electronic Press, Sweden, pp. 41--57, 2013) Haverinen, Katri; Laippala, Veronika; Kohonen, Samuel; Missilä, Anna; Nyblom, Jenna; Ojala, Stina; Viljanen, Timo; Salakoski, Tapio; Ginter, Filip; Oepen, Stephan; Hagen, Kristin; Johannessen, Janne Bondi
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Towards the Classification of the Finnish Internet Parsebank: Detecting Translations and Informality
(Vilnius, Lithuania, Linköping University Electronic Press, Sweden, pp. 107--116, 2015) Laippala, Veronika; Kanerva, Jenna; Missilä, Anna; Pyysalo, Sampo; Salakoski, Tapio; Ginter, Filip; Megyesi, Beáta
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Toxicity Detection in Finnish Using Machine Translation
(University of Tartu Library, 2023-05) Eskelinen, Anni; Silvala, Laura; Ginter, Filip; Pyysalo, Sampo; Laippala, Veronika
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Treebanking Finnish
(2010-11-29) Haverinen, Katri; Viljanen, Timo; Laippala, Veronika; Kohonen, Samuel; Ginter, Filip; Salakoski, Tapio
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Universal Dependencies for Finnish
(Vilnius, Lithuania, Linköping University Electronic Press, Sweden, pp. 163--172, 2015) Pyysalo, Sampo; Kanerva, Jenna; Missilä, Anna; Laippala, Veronika; Ginter, Filip; Megyesi, Beáta