LSCDiscovery: A shared task on semantic change discovery and detection in Spanish

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AW8BJSJYR" target="_blank" >RIV/00216208:11320/22:W8BJSJYR - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2022.lchange-1.16" target="_blank" >https://aclanthology.org/2022.lchange-1.16</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2022.lchange-1.16" target="_blank" >10.18653/v1/2022.lchange-1.16</a>

Jazyk výsledku

angličtina

Název v původním jazyce

LSCDiscovery: A shared task on semantic change discovery and detection in Spanish

Popis výsledku v původním jazyce

We present the first shared task on semantic change discovery and detection in Spanish. We create the first dataset of Spanish words manually annotated by semantic change using the DURel framewok (Schlechtweg et al., 2018). The task is divided in two phases: 1) graded change discovery, and 2) binary change detection. In addition to introducing a new language for this task, the main novelty with respect to the previous tasks consists in predicting and evaluating changes for all vocabulary words in the corpus. Six teams participated in phase 1 and seven teams in phase 2 of the shared task, and the best system obtained a Spearman rank correlation of 0.735 for phase 1 and an F1 score of 0.735 for phase 2. We describe the systems developed by the competing teams, highlighting the techniques that were particularly useful.

Název v anglickém jazyce

LSCDiscovery: A shared task on semantic change discovery and detection in Spanish

Popis výsledku anglicky

We present the first shared task on semantic change discovery and detection in Spanish. We create the first dataset of Spanish words manually annotated by semantic change using the DURel framewok (Schlechtweg et al., 2018). The task is divided in two phases: 1) graded change discovery, and 2) binary change detection. In addition to introducing a new language for this task, the main novelty with respect to the previous tasks consists in predicting and evaluating changes for all vocabulary words in the corpus. Six teams participated in phase 1 and seven teams in phase 2 of the shared task, and the best system obtained a Spearman rank correlation of 0.735 for phase 1 and an F1 score of 0.735 for phase 2. We describe the systems developed by the competing teams, highlighting the techniques that were particularly useful.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

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Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change

ISBN

978-1-955917-42-1

ISSN

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e-ISSN

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Počet stran výsledku

16

Strana od-do

149-164

Název nakladatele

Association for Computational Linguistics

Místo vydání

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Místo konání akce

Dublin, Ireland

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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