@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://preview.aclanthology.org/iwcs-25-ingestion/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."
}