sis 18(16): e3

Research Article

Big Data and Named Entity Recognition Approaches for Urdu Language

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  • @ARTICLE{10.4108/eai.13-4-2018.154469,
    author={Qudsia Jamil  and Muhammad  Rehman Zafar },
    title={Big Data and Named Entity Recognition Approaches for Urdu Language},
    journal={EAI Endorsed Transactions on Scalable Information Systems},
    volume={5},
    number={16},
    publisher={EAI},
    journal_a={SIS},
    year={2018},
    month={4},
    keywords={Big Data, Named Entity Recognition, Urdu Text Processing, Natural Language Processing(NLP)},
    doi={10.4108/eai.13-4-2018.154469}
}
  • Qudsia Jamil
    Muhammad Rehman Zafar
    Year: 2018
    Big Data and Named Entity Recognition Approaches for Urdu Language
    SIS
    EAI
    DOI: 10.4108/eai.13-4-2018.154469
Qudsia Jamil 1,*, Muhammad Rehman Zafar 1,*
  • 1: Department of Computer Science Bahria University, Islamabad, Pakistan
*Contact email: qudsi.ch@gmail.com , rehmanzafar.bui@gmail.com

Abstract

Nowadays data is stored in digital form and Terabyte of data is generated on daily basis. It is difficult task to extract useful information from Big data efficiently. From unstructured text Information extraction is a technique which used to extract information. Named Entity Recognition (NER) is an essential component of information extraction in the field of Natural Language Processing (NLP). Further, Urdu language has various challenges to NER due to its agglutinative, inflectional nature and rich morphology. Therefore, NER systems for Urdu language are not mature yet due to lack of resources and ambiguities. This paper specifically addresses the different approaches to NER and explore the existing work for NER in Urdu language.

Keywords
Big Data, Named Entity Recognition, Urdu Text Processing, Natural Language Processing(NLP)
Received
2017-10-13
Accepted
2018-01-03
Published
2018-04-13
Publisher
EAI
http://dx.doi.org/10.4108/eai.13-4-2018.154469

Copyright © 2018 Qudsia Jamil and Muhammad Rehman Zafar, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.