Topical Text Classification of Russian News: a Comparison of BERT and Standard Models
The paper is devoted to the single-label topical classification of Russian news. The author compares the BERT features and standard character, word and structure-level features as text models. Experiments with OpenCorpora show that the BERT model is superior to standard ones, and achieves good class...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ksenia Lagutina [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Proceedings of the XXth Conference of Open Innovations Association FRUCT - FRUCT, 2017, 31(2022), 1, Seite 160-166 |
|---|---|
| Übergeordnetes Werk: |
volume:31 ; year:2022 ; number:1 ; pages:160-166 |
| Links: |
Link aufrufen |
|---|
| DOI / URN: |
10.23919/FRUCT54823.2022.9770920 |
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The paper is devoted to the single-label topical classification of Russian news. The author compares the BERT features and standard character, word and structure-level features as text models. Experiments with OpenCorpora show that the BERT model is superior to standard ones, and achieves good classification quality for a small dataset of long news. Comparison with the state-of-the-art research allows to consider BERT as a baseline for future investigations of analysis of texts in Russian. |
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