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        <title>Universal Dependencies Parsing for Colloquial Singaporean English</title>
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    <name type="personal">
        <namePart type="given">Hongmin</namePart>
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    <abstract>Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.</abstract>
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%0 Conference Proceedings
%T Universal Dependencies Parsing for Colloquial Singaporean English
%A Wang, Hongmin
%A Zhang, Yue
%A Chan, GuangYong Leonard
%A Yang, Jie
%A Chieu, Hai Leong
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F wang-etal-2017-universal
%X Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.
%R 10.18653/v1/P17-1159
%U https://aclanthology.org/P17-1159
%U https://doi.org/10.18653/v1/P17-1159
%P 1732-1744

Markdown (Informal)

[Universal Dependencies Parsing for Colloquial Singaporean English](https://aclanthology.org/P17-1159) (Wang et al., ACL 2017)

• Universal Dependencies Parsing for Colloquial Singaporean English (Wang et al., ACL 2017)

ACL

• Hongmin Wang, Yue Zhang, GuangYong Leonard Chan, Jie Yang, and Hai Leong Chieu. 2017. Universal Dependencies Parsing for Colloquial Singaporean English. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1732–1744, Vancouver, Canada. Association for Computational Linguistics.