@inproceedings{sikdar-etal-2018-named,
    title = "Named Entity Recognition on Code-Switched Data Using Conditional Random Fields",
    author = {Sikdar, Utpal Kumar  and
      Barik, Biswanath  and
      Gamb{\"a}ck, Bj{\"o}rn},
    editor = "Aguilar, Gustavo  and
      AlGhamdi, Fahad  and
      Soto, Victor  and
      Solorio, Thamar  and
      Diab, Mona  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W18-3215/",
    doi = "10.18653/v1/W18-3215",
    pages = "115--119",
    abstract = "Named Entity Recognition is an important information extraction task that identifies proper names in unstructured texts and classifies them into some pre-defined categories. Identification of named entities in code-mixed social media texts is a more difficult and challenging task as the contexts are short, ambiguous and often noisy. This work proposes a Conditional Random Fields based named entity recognition system to identify proper names in code-switched data and classify them into nine categories. The system ranked fifth among nine participant systems and achieved a 59.25{\%} F1-score."
}