@inproceedings{claeser-etal-2018-multilingual,
    title = "Multilingual Named Entity Recognition on {S}panish-{E}nglish Code-switched Tweets using Support Vector Machines",
    author = "Claeser, Daniel  and
      Kent, Samantha  and
      Felske, Dennis",
    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/W18-3218/",
    doi = "10.18653/v1/W18-3218",
    pages = "132--137",
    abstract = "This paper describes our system submission for the ACL 2018 shared task on named entity recognition (NER) in code-switched Twitter data. Our best result (F1 = 53.65) was obtained using a Support Vector Machine (SVM) with 14 features combined with rule-based post processing."
}