Using an SVM Ensemble System for Improved Tamil Dependency Parsing

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Using an SVM Ensemble System for Improved Tamil Dependency Parsing

Popis výsledku v původním jazyce

Dependency parsing has been shown to improve NLP systems in certain languages and in many cases helps achieve state of the art results in NLP applications, in particular applications for free word order languages. Morphologically rich languages are oftenshort on training data or require much higher amounts of training data due to the increased size of their lexicon. This paper examines a new approach for addressing morphologically rich languages with little training data to start. Using Tamil as our test language, we create 9 dependency parse models with a limited amount of training data. Using these models we train an SVM classifier using only the model agreements as features. We use this SVM classifier on an edge by edge decision to form an ensembleparse tree. Using only model agreements as features allows this method to remain language independent and applicable to a wide range of morphologically rich languages. We show a statistically significant 5.44% improvement over the averag

Název v anglickém jazyce

Using an SVM Ensemble System for Improved Tamil Dependency Parsing

Popis výsledku anglicky

Dependency parsing has been shown to improve NLP systems in certain languages and in many cases helps achieve state of the art results in NLP applications, in particular applications for free word order languages. Morphologically rich languages are oftenshort on training data or require much higher amounts of training data due to the increased size of their lexicon. This paper examines a new approach for addressing morphologically rich languages with little training data to start. Using Tamil as our test language, we create 9 dependency parse models with a limited amount of training data. Using these models we train an SVM classifier using only the model agreements as features. We use this SVM classifier on an edge by edge decision to form an ensembleparse tree. Using only model agreements as features allows this method to remain language independent and applicable to a wide range of morphologically rich languages. We show a statistically significant 5.44% improvement over the averag

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages

ISBN

978-1-937284-30-5

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

72-77

Název nakladatele

Association for Computational Linguistics

Místo vydání

Jeju, Korea

Místo konání akce

Jeju, Korea

Datum konání akce

12. 7. 2012

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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