Accurate Dependency Parsing and Tagging of Latin
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AMYEBKE6C" target="_blank" >RIV/00216208:11320/22:MYEBKE6C - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2022.lt4hala-1.3" target="_blank" >https://aclanthology.org/2022.lt4hala-1.3</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Accurate Dependency Parsing and Tagging of Latin
Original language description
Having access to high-quality grammatical annotations is important for downstream tasks in NLP as well as for corpus-based research. In this paper, we describe experiments with the Latin BERT word embeddings that were recently be made available by Bamman and Burns (2020). We show that these embeddings produce competitive results in the low-level task of morpho-syntactic tagging. In addition, we describe a graph-based dependency parser that is trained with these embeddings and clearly outperforms various baselines.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2022)
ISBN
979-10-95546-78-8
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
20-25
Publisher name
European Language Resources Association
Place of publication
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Event location
Marseille, France
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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