Named Entity Recognition is the methodology of distinguishing named entities which are present in documents. NER mostly used and applied for information extraction. In this paper, a system comprising two approaches for NER has been proposed for Marathi.

Random Marathi text documents are given as an input to the system. These documents are from news domain which consist text related to history, sports, entertainment etc. Input text is processed to create an annotated NER tagged data set by linguistic experts. All the input documents will be trained first and then tested in HMM based approach. In the training phase of HMM the annotated text is tokenized and all the parameters for HMM like transition, emission, etc. are computed. In the testing phase correct NER tag is predicted using Viterbi algorithm. In the Rule Based approach first the input is tokenized and POS tagged. By using the NER tagset tokens in the sentences are assigned correct NER tags. The words which are left to be tagged are then tagged by using handcrafted rules. Finally the system output is NER tag sentences.


Named entity recognition HMM Rule based NER 


  1. 1.
    Morwal, S., Jahan, N., Chopra, D.: Named entity recognition using Hidden Markov Model (HMM). Int. J. Nat. Lang. Comput. (IJNLC), 1(4), December 2012 CrossRefGoogle Scholar
  2. 2.
    Maxwell, J.C.: A Treatise on Electricity and Magnetism, vol. 2, 3rd edn, pp. 68–73. Oxford, Clarendon (1892)Google Scholar
  3. 3.
    Chopra, D., Joshi, N., Mathur, I.: Named entity recognition in Hindi Using Hidden Markov Model. In: Second International Conference on Computational Intelligence and Communication Technology (2016). 978-1-5090-0210-8/16 $31.00 © 2016 IEEE. K. Elissa, “Title of paper if known,” unpublishedGoogle Scholar
  4. 4.
    Morwal, S., Jahan, N.. Named entity recognition using Hidden Markov Model (HMM): an experimental result on Hindi, Urdu and Marathi languages. In: Advanced Computational Intelligence: An International Journal (ACII), vol. 2, no.4, October 2015Google Scholar
  5. 5.
    Singh, U., Goyal, V., Lehal, G.S.: Named entity recognition system for Urdu. In: Proceedings of COLING 2012: Technical Papers, pp. 2507–2518, COLING 2012, Mumbai, December 2012Google Scholar
  6. 6.
    Kaur, Y., Kaur, E.R.: Named Entity Recognition (NER) system for Hindi language using combination of rule based approach and list look up approach. Int. J. Sci. Res. Manag. (IJSRM) 3(3), 2300–2306 (2015). ISSN (e): 2321-3418. www.ijsrm.inGoogle Scholar
  7. 7.
  8. 8.
    Govilkar, S.S., Bakal, J.W., Kulkarni, S.R.: Extraction of root words using morphological analyzer for devanagari script. Int. J. Inf. Technol. Comput. Sci. 01, 33–39 (2016). Published Online January 2016 in MECS ( Scholar
  9. 9.
    Govilkar, S., Bakal, J.W., Rathod, S.: Part of speech tagger for Marathi language. Int. J. Comput. Appl. (0975–8887), 119(18), June 2015CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Computer EngineeringPCE, University of MumbaiNavi MumbaiIndia

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