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1
Machine-readable Finnish-Livvi bilingual translation dictionary ...
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2
Machine-readable Finnish-Karelian bilingual translation dictionary ...
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3
Machine-readable Finnish-Karelian bilingual translation dictionary ...
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4
Machine-readable Northern Karelian Proper-Livvi bilingual translation dictionary ...
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5
Machine-readable Finnish-Livvi bilingual translation dictionary ...
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6
Machine-readable Northern Karelian Proper-Livvi bilingual translation dictionary ...
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7
Olonets-Karelian-to-X XML Dictionary ...
Rantakaulio, Timo; Rueter, Jack. - : Zenodo, 2022
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8
Olonets-Karelian-to-X XML Dictionary ...
Rantakaulio, Timo; Rueter, Jack. - : Zenodo, 2022
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9
Data for Finnish Dialect Detection ...
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10
Data for Finnish Dialect Detection ...
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11
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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12
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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13
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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14
Finnish Rumor Detection Dataset and Models ...
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15
Finnish Rumor Detection Dataset and Models ...
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16
Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered ...
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17
Processing M.A. Castrén's Materials: Multilingual Typed and Handwritten Manuscripts ...
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18
Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline ...
Abstract: This study presents a new dataset on rumor detection in Finnish language news headlines. We have evaluated two different LSTM based models and two different BERT models, and have found very significant differences in the results. A fine-tuned FinBERT reaches the best overall accuracy of 94.3% and rumor label accuracy of 96.0% of the time. However, a model fine-tuned on Multilingual BERT reaches the best factual label accuracy of 97.2%. Our results suggest that the performance difference is due to a difference in the original training data. Furthermore, we find that a regular LSTM model works better than one trained with a pretrained word2vec model. These findings suggest that more work needs to be done for pretrained models in Finnish language as they have been trained on small and biased corpora. ... : 2021 Workshop on NLP4IF: Censorship, Disinformation, and Propaganda ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2106.03389
https://dx.doi.org/10.48550/arxiv.2106.03389
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19
rueter/aku-morph-lushootseed: mutual verb and noun morphology ...
Rueter, Jack. - : Zenodo, 2021
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20
rueter/aku-morph-lushootseed: mutual verb and noun morphology ...
Rueter, Jack. - : Zenodo, 2021
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