@inproceedings{pal-sharma-2019-towards,
title = "Towards Automated Semantic Role Labelling of {H}indi-{E}nglish Code-Mixed Tweets",
author = "Pal, Riya and
Sharma, Dipti",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5538",
doi = "10.18653/v1/D19-5538",
pages = "291--296",
abstract = "We present a system for automating Semantic Role Labelling of Hindi-English code-mixed tweets. We explore the issues posed by noisy, user generated code-mixed social media data. We also compare the individual effect of various linguistic features used in our system. Our proposed model is a 2-step system for automated labelling which gives an overall accuracy of 84{\%} for Argument Classification, marking a 10{\%} increase over the existing rule-based baseline model. This is the first attempt at building a statistical Semantic Role Labeller for Hindi-English code-mixed data, to the best of our knowledge.",
}
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%0 Conference Proceedings
%T Towards Automated Semantic Role Labelling of Hindi-English Code-Mixed Tweets
%A Pal, Riya
%A Sharma, Dipti
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F pal-sharma-2019-towards
%X We present a system for automating Semantic Role Labelling of Hindi-English code-mixed tweets. We explore the issues posed by noisy, user generated code-mixed social media data. We also compare the individual effect of various linguistic features used in our system. Our proposed model is a 2-step system for automated labelling which gives an overall accuracy of 84% for Argument Classification, marking a 10% increase over the existing rule-based baseline model. This is the first attempt at building a statistical Semantic Role Labeller for Hindi-English code-mixed data, to the best of our knowledge.
%R 10.18653/v1/D19-5538
%U https://aclanthology.org/D19-5538
%U https://doi.org/10.18653/v1/D19-5538
%P 291-296
Markdown (Informal)
[Towards Automated Semantic Role Labelling of Hindi-English Code-Mixed Tweets](https://aclanthology.org/D19-5538) (Pal & Sharma, WNUT 2019)
ACL