Deep learning based parts of speech tagger for Bengali

Md Fasihul Kabir, Khandaker Abdullah-Al-Mamun, Mohammad Nurul Huda

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    14 Scopus citations

    Abstract

    This paper describes the Part of Speech (POS) tagger for Bengali Language. Here, POS tagging is the process of assigning the part of speech tag or other lexical class marker to each and every word in a sentence. In many Natural Language Processing (NLP) applications, POS tagging is considered as the one of the basic necessary tools. Identifying the ambiguities in language lexical items is the challenging objective in the process of developing an efficient and accurate POS Tagger. Different methods of automating the process have been developed and employed for Bengali. In this paper, we report about our work on building POS tagger for Bengali using the Deep Learning. Bengali is a morphologically rich language and our taggers make use of morphological and contextual information of the words. It is observed from the experiments based on Linguistic Data Consortium (LDC) catalog number LDC2010T16 and ISBN 1-58563-561-8 corpus that 93.33% accuracy is obtained for Bengali POS tagger using the Deep Learning.

    Original languageEnglish (US)
    Title of host publication2016 5th International Conference on Informatics, Electronics and Vision, ICIEV 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages26-29
    Number of pages4
    ISBN (Electronic)9781509012695
    DOIs
    StatePublished - Nov 28 2016
    Event5th International Conference on Informatics, Electronics and Vision, ICIEV 2016 - Dhaka, Bangladesh
    Duration: May 13 2016May 14 2016

    Publication series

    Name2016 5th International Conference on Informatics, Electronics and Vision, ICIEV 2016

    Conference

    Conference5th International Conference on Informatics, Electronics and Vision, ICIEV 2016
    CountryBangladesh
    CityDhaka
    Period5/13/165/14/16

    All Science Journal Classification (ASJC) codes

    • Computer Networks and Communications
    • Artificial Intelligence
    • Computer Science Applications
    • Computer Vision and Pattern Recognition
    • Human-Computer Interaction
    • Information Systems
    • Electrical and Electronic Engineering
    • Instrumentation

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