title: Language-based transfer learning approaches for part-of-speech tagging on Saint Petersburg Corpus of Hagiographic texts (SKAT)
metadata.name: dc.contributor.author
metadata.value: Gudkov, Vadim V.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Mitrenina, Olga V.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Sokolov, Evgenii G.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Koval, Angelina A.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2023-10-10T11:25:46Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2023-10-10T11:25:46Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2023-06
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Gudkov V. V., Mitrenina O. V., Sokolov E. G., Koval A. A. Language-based transfer learning approaches for part-of-speech tagging on Saint Petersburg Corpus of Hagiographic texts (SKAT). Vestnik of Saint Petersburg University. Language and Literature. 2023, 20 (2): 268–282. https://doi.org/10.21638/spbu09.2023.205 (In Russian)
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu09.2023.205
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/44183
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The article describes an experiment about training a part-of-speech tagger using artificial
  neural networks on the St. Petersburg Corpus of Hagiographic Texts (SKAT), which is being
  developed at the Department of Mathematical Linguistics of St. Petersburg State University.
  The corpus includes the texts of 23 manuscripts dating from the 15th–18th centuries with about
  190,000 words usages, four of which were labelled manually. The bi-LSTM, distilled RuBERTtiny2
  and RuBERT models were used to train a POS tagger. All of them were trained on modern
  Russian corpora and further fine-tuned to label Old Russian texts using a technique called
  language transfer. To fine-tune transformer-based language models it was necessary to tokenize
  the texts using byte pair encoding and map tokens from the original Russian-language
  tokenizer to the new one based on indices. Then the model was fine-tuned for the token classification
  task. To fine-tune the model, a tagged subcorpus of three hagiographical texts was
  used, which included 35,603 tokens and 2,885 sentences. The experiment took into account
  only the tags of the parts of speech, the classification included seventeen tags, thirteen of
  which corresponded to parts of speech, and the remaining four marked punctuation marks.
  To evaluate the quality of the model, the standard metrics F1 and Accuracy were used. According
  to automatic evaluation metrics, the RuBERT model showed the best result. Most of
  the errors were related to incorrect generalization of linear position patterns or to the similarity
  of word forms in both the extreme left and extreme right positions.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Language and Literature;Volume 20; Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: hagiography
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: corpus of Old Russian texts
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: neural network tagging
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: language-based transfer learning
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: part-of speech tagging
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Language-based transfer learning approaches for part-of-speech tagging on Saint Petersburg Corpus of Hagiographic texts (SKAT)
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 05.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/44183/1/05.pdf
resources.format: Adobe PDF
resources.size: 742,78 kB