@inproceedings{al-badrashiny-diab-2016-lili,
    title = "{LILI}: A Simple Language Independent Approach for Language Identification",
    author = "Al-Badrashiny, Mohamed  and
      Diab, Mona",
    editor = "Matsumoto, Yuji  and
      Prasad, Rashmi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/C16-1115/",
    pages = "1211--1219",
    abstract = "We introduce a generic Language Independent Framework for Linguistic Code Switch Point Detection. The system uses characters level 5-grams and word level unigram language models to train a conditional random fields (CRF) model for classifying input words into various languages. We test our proposed framework and compare it to the state-of-the-art published systems on standard data sets from several language pairs: English-Spanish, Nepali-English, English-Hindi, Arabizi (Refers to Arabic written using the Latin/Roman script)-English, Arabic-Engari (Refers to English written using Arabic script), Modern Standard Arabic(MSA)-Egyptian, Levantine-MSA, Gulf-MSA, one more English-Spanish, and one more MSA-EGY. The overall weighted average F-score of each language pair are 96.4{\%}, 97.3{\%}, 98.0{\%}, 97.0{\%}, 98.9{\%}, 86.3{\%}, 88.2{\%}, 90.6{\%}, 95.2{\%}, and 85.0{\%} respectively. The results show that our approach despite its simplicity, either outperforms or performs at comparable levels to state-of-the-art published systems."
}