Data-driven dependency parsing of Vedic Sanskrit

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A89ADZUUF" target="_blank" >RIV/00216208:11320/23:89ADZUUF - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147913999&doi=10.1007%2fs10579-023-09636-5&partnerID=40&md5=e544a6c65f3d5abce2be76fe755f4aea" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147913999&doi=10.1007%2fs10579-023-09636-5&partnerID=40&md5=e544a6c65f3d5abce2be76fe755f4aea</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10579-023-09636-5" target="_blank" >10.1007/s10579-023-09636-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-driven dependency parsing of Vedic Sanskrit

Popis výsledku v původním jazyce

"This paper describes the first data-driven parser for Vedic Sanskrit, an ancient Indo-Aryan language in which a corpus of important religious and philosophical texts has been composed. We report and critically discuss experiments with the input feature representations, paying special attention to the performance of contextualized word embeddings and to the influence of morpho-syntactic representations on the parsing quality. In addition, we provide an in-depth discussion of the parsing errors that covers structural traits of the predicted trees as well as linguistic and extra-textual influence factors. In its optimal configuration, the proposed model achieves 87.61 unlabeled and 81.84 labeled attachment score on a held-out set of test sentences, demonstrating good performance for an under-resourced language. © 2023, The Author(s)."

Název v anglickém jazyce

Data-driven dependency parsing of Vedic Sanskrit

Popis výsledku anglicky

"This paper describes the first data-driven parser for Vedic Sanskrit, an ancient Indo-Aryan language in which a corpus of important religious and philosophical texts has been composed. We report and critically discuss experiments with the input feature representations, paying special attention to the performance of contextualized word embeddings and to the influence of morpho-syntactic representations on the parsing quality. In addition, we provide an in-depth discussion of the parsing errors that covers structural traits of the predicted trees as well as linguistic and extra-textual influence factors. In its optimal configuration, the proposed model achieves 87.61 unlabeled and 81.84 labeled attachment score on a held-out set of test sentences, demonstrating good performance for an under-resourced language. © 2023, The Author(s)."

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

"Language Resources and Evaluation"

ISSN

1574-020X

e-ISSN

—

Svazek periodika

57

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

34

Strana od-do

1173-1206

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85147913999