IDEAS home Printed from https://ideas.repec.org/a/eur/ejlsjr/107.html
   My bibliography  Save this article

Automatic Language Identification

Author

Listed:
  • Nejla Qafmolla

    (Tirana University, Faculty of Foreign Languages, English Department - Tirana, Albania)

Abstract

Automatic Language Identification (LID) is the process of automatically identifying the language of spoken utterance or written material. LID has received much attention due to its application to major areas of research and long-aspired dreams in computational sciences, namely Machine Translation (MT), Speech Recognition (SR) and Data Mining (DM). A considerable increase in the amount of and access to data provided not only by experts but also by users all over the Internet has resulted into both the development of different approaches in the area of LID - so as to generate more efficient systems - as well as major challenges that are still in the eye of the storm of this field. Despite the fact that the current approaches have accomplished considerable success, future research concerning some issues remains on the table. The aim of this paper shall not be to describe the historic background of this field of studies, but rather to provide an overview of the current state of LID systems, as well as to classify the approaches developed to accomplish them. LID systems have advanced and are continuously evolving. Some of the issues that need special attention and improvement are semantics, the identification of various dialects and varieties of a language, identification of spelling errors, data retrieval, multilingual documents, MT and speech-to-speech translation. Methods applied to date have been good from a technical point of view, but not from a semantic one.

Suggested Citation

  • Nejla Qafmolla, 2017. "Automatic Language Identification," European Journal of Language and Literature Studies Articles, Revistia Research and Publishing, vol. 3, January -.
  • Handle: RePEc:eur:ejlsjr:107
    DOI: 10.26417/ejls.v7i1.p140-150
    as

    Download full text from publisher

    File URL: https://revistia.org/index.php/ejls/article/view/5752
    Download Restriction: no

    File URL: https://revistia.org/files/articles/ejls_v3_i1_17/Nejla.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.26417/ejls.v7i1.p140-150?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eur:ejlsjr:107. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Revistia Research and Publishing (email available below). General contact details of provider: https://revistia.org/index.php/ejls .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.