• DocumentCode
    1776961
  • Title

    POSABL: ABL-based grammar induction on POS tags

  • Author

    Passban, Peyman ; Shokrollahi-Far, Mahmoud

  • Author_Institution
    Language & Knowledge Eng. Lab., Univ. Coll. of Nabi Akram, Tabriz, Iran
  • fYear
    2014
  • fDate
    29-30 Oct. 2014
  • Firstpage
    285
  • Lastpage
    291
  • Abstract
    Grammar Induction (GI) is the problem of extracting hidden regularities and syntactic patterns in languages. Not only the manner of extraction is intricate but also the definition of meaningful patterns is a challenge. Alignment Based Learning (ABL) is one of the research endeavors targeting such challenges in GI. Our present research on applying ABL to POS sequences in English, Persian and Arabic languages has led us to POSABL yielding the enhanced recall of 83%, 74% and 100% for the three languages, respectively. Our experimental results on minimizing the size of the tag-set and the maximum size of train data required for our GI are considerable, as well. This research trend on POS sequences is hoped to pave the way more for GI on different languages of creation, say DNA sequences.
  • Keywords
    grammars; learning (artificial intelligence); natural language processing; ABL-based grammar induction; Arabic languages; DNA sequences; English languages; GI; POS sequences; POS tags; POSABL; Persian languages; alignment based learning; grammar induction; syntactic patterns; tag-set size; Accuracy; Educational institutions; Grammar; Pragmatics; Syntactics; Training; US Department of Transportation; binary grammar alignmnet; grammar induction; syntactic patersn;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Knowledge Engineering (ICCKE), 2014 4th International eConference on
  • Conference_Location
    Mashhad
  • Print_ISBN
    978-1-4799-5486-5
  • Type

    conf

  • DOI
    10.1109/ICCKE.2014.6993382
  • Filename
    6993382