Yet Another Language Identifier

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10130078" target="_blank" >RIV/00216208:11320/12:10130078 - isvavai.cz</a>

Výsledek na webu

<a href="http://aclweb.org/anthology-new/E/E12/E12-3006.pdf" target="_blank" >http://aclweb.org/anthology-new/E/E12/E12-3006.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Yet Another Language Identifier

Popis výsledku v původním jazyce

Language identification of written text has been studied for several decades. Despite this fact, most of the research is focused on a few most spoken languages, whereas the minor ones are ignored. The identification of a larger number of languages bringsnew difficulties that do not occur for a few languages. These difficulties are causing decreased accuracy. The objective of this paper is to investigate the sources of such degradation. In order to isolate the impact of individual factors, 5 different algorithms and 3 different number of languages are used. The Support Vector Machine algorithm achieved an accuracy of 98% for 90 languages and the YALI algorithm based on a scoring function had an accuracy of 95.4%. The YALI algorithm has slightly lower accuracy but classifies around 17 times faster and its training is more than 4000 times faster. Three different data sets with various number of languages and sample sizes were prepared to overcome the lack of standardized data sets. These

Název v anglickém jazyce

Yet Another Language Identifier

Popis výsledku anglicky

Language identification of written text has been studied for several decades. Despite this fact, most of the research is focused on a few most spoken languages, whereas the minor ones are ignored. The identification of a larger number of languages bringsnew difficulties that do not occur for a few languages. These difficulties are causing decreased accuracy. The objective of this paper is to investigate the sources of such degradation. In order to isolate the impact of individual factors, 5 different algorithms and 3 different number of languages are used. The Support Vector Machine algorithm achieved an accuracy of 98% for 90 languages and the YALI algorithm based on a scoring function had an accuracy of 95.4%. The YALI algorithm has slightly lower accuracy but classifies around 17 times faster and its training is more than 4000 times faster. Three different data sets with various number of languages and sample sizes were prepared to overcome the lack of standardized data sets. These

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/7E11042" target="_blank" >7E11042: Knowledge Helper for Medical and Other Information users</a><br>

Návaznosti

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

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics

ISBN

978-1-937284-19-0

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

46-54

Název nakladatele

Association for Computational Linguistics

Místo vydání

Avignon, France

Místo konání akce

Avignon, France

Datum konání akce

23. 4. 2012

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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