LSCP: Enhanced Large Scale Colloquial Persian Language Understanding

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10424427" target="_blank" >RIV/00216208:11320/20:10424427 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.aclweb.org/anthology/2020.lrec-1.776" target="_blank" >https://www.aclweb.org/anthology/2020.lrec-1.776</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

LSCP: Enhanced Large Scale Colloquial Persian Language Understanding

Popis výsledku v původním jazyce

Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Persian. In order to target this gap for low resource languages, we propose a &quot;Large Scale Colloquial Persian Dataset&quot; (LSCP). LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. We believe that further investigations and processing, as well as the application of novel algorithms and methods, can strengthen enriching computerized understanding and processing of low resource languages. The proposed corpus cons

Název v anglickém jazyce

LSCP: Enhanced Large Scale Colloquial Persian Language Understanding

Popis výsledku anglicky

Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Persian. In order to target this gap for low resource languages, we propose a &quot;Large Scale Colloquial Persian Dataset&quot; (LSCP). LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. We believe that further investigations and processing, as well as the application of novel algorithms and methods, can strengthen enriching computerized understanding and processing of low resource languages. The proposed corpus cons

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

<a href="/cs/project/GX19-26934X" target="_blank" >GX19-26934X: Neuronové reprezentace v multimodálním a mnohojazyčném modelování</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)

ISBN

979-10-95546-34-4

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

6323-6327

Název nakladatele

European Language Resources Association

Místo vydání

Marseille, France

Místo konání akce

Marseille, France

Datum konání akce

11. 5. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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