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Synthetic Treebanking for Cross-Lingual Dependency Parsing / Tiedemann, Jörg ; Agić, Željko.

By: Tiedemann, Jörg.
Contributor(s): Agić, Željko [aut].
Material type: ArticleArticlePublisher: 2015Description: 209-248 str.Other title: Synthetic Treebanking for Cross-Lingual Dependency Parsing [Naslov na engleskom:].Subject(s): parsing | strojno prevođenje | NLP | 5.04Online resources: Click here to access online In: The journal of artificial intelligence research 55 (2015) ; str. 209-248Summary: How do we parse the languages for which no treebanks are available? This contribution addresses the cross-lingual viewpoint on statistical dependency parsing, in which we attempt to make use of resource-rich source language treebanks to build and adapt models for the under-resourced target languages. We outline the benefits, and indicate the drawbacks of the current major approaches. We emphasize synthetic treebanking: the automatic creation of target language treebanks by means of annotation projection and machine translation. We present competitive results in cross-lingual dependency parsing using a combination of various techniques that contribute to the overall success of the method. We further include a detailed discussion about the impact of part-of-speech label accuracy on parsing results that provide guidance in practical applications of cross-lingual methods for truly under-resourced languages.
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How do we parse the languages for which no treebanks are available? This contribution addresses the cross-lingual viewpoint on statistical dependency parsing, in which we attempt to make use of resource-rich source language treebanks to build and adapt models for the under-resourced target languages. We outline the benefits, and indicate the drawbacks of the current major approaches. We emphasize synthetic treebanking: the automatic creation of target language treebanks by means of annotation projection and machine translation. We present competitive results in cross-lingual dependency parsing using a combination of various techniques that contribute to the overall success of the method. We further include a detailed discussion about the impact of part-of-speech label accuracy on parsing results that provide guidance in practical applications of cross-lingual methods for truly under-resourced languages.

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