Towards Automated Semantic Role Labelling of Hindi-English Code-Mixed Tweets

Riya Pal, Dipti Sharma


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.
Anthology ID:
D19-5538
Volume:
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
291–296
Language:
URL:
https://www.aclweb.org/anthology/D19-5538
DOI:
10.18653/v1/D19-5538
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5538.pdf