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A Memory-Based Learning Approach for Named Entity Recognition in Hindi

Journal of intelligent systems, 2017-04, Vol.26 (2), p.301-321 [Peer Reviewed Journal]

Copyright Walter de Gruyter GmbH 2017 ;ISSN: 0334-1860 ;EISSN: 2191-026X ;DOI: 10.1515/jisys-2015-0010

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  • Title:
    A Memory-Based Learning Approach for Named Entity Recognition in Hindi
  • Author: Sarkar, Kamal ; Shaw, Sudhir Kumar
  • Subjects: Comparative studies ; HMM ; Learning ; Markov analysis ; memory-based learning ; Named entity recognition (NER) ; natural language processing
  • Is Part Of: Journal of intelligent systems, 2017-04, Vol.26 (2), p.301-321
  • Description: Named entity (NE) recognition (NER) is a process to identify and classify atomic elements such as person name, organization name, place/location name, quantities, temporal expressions, and monetary expressions in running text. In this paper, the Hindi NER task has been mapped into a multiclass learning problem, where the classes are NE tags. This paper presents a solution to this Hindi NER problem using a memory-based learning method. A set of simple and composite features, which includes binary, nominal, and string features, has been defined and incorporated into the proposed model. A relatively small Hindi Gazetteer list has also been employed to enhance the system performance. A comparative study on the experimental results obtained by the memory-based NER system proposed in this paper and a hidden Markov model (HMM)-based NER system shows that the performance of the proposed memory-based NER system is comparable to the HMM-based NER system.
  • Publisher: Berlin: De Gruyter
  • Language: English
  • Identifier: ISSN: 0334-1860
    EISSN: 2191-026X
    DOI: 10.1515/jisys-2015-0010
  • Source: De Gruyter Open Access Journals
    ProQuest Central
    DOAJ Directory of Open Access Journals

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