Indonesian Dependency Treebank: Annotation and Parsing

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10130048" target="_blank" >RIV/00216208:11320/12:10130048 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Indonesian Dependency Treebank: Annotation and Parsing
Original language description
We also show ensemble dependency parsing and self training approaches applicable to under-resourced languages using our manually annotated dependency structures. We show that for an under-resourced language, the use of tuning data for a meta classifier is more effective than using it as additional training data for individual parsers. This meta-classifier creates an ensemble dependency parser and increases the dependency accuracy by 4.92% on average and 1.99% over the best individual models on average.As the data sizes grow for the the under-resourced language a meta classifier can easily adapt. To the best of our knowledge this is the first full implementation of a dependency parser for Indonesian. Using self-training in combination with our EnsembleSVM Parser we show additional improvement. Using this parsing model we plan on expanding the size of the corpus by using a semi-supervised approach by applying the parser and correcting the errors, reducing the amount of annotation time
Czech name
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Czech description
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Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 26th Pacific Asia Conference on Language, Information and Computation
ISBN
978-979-1421-17-1
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
137-145
Publisher name
Faculty of Computer Science, Universitas Indonesia
Place of publication
Bali, Indonesia
Event location
Bali, Indonesia
Event date
Nov 7, 2012
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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