Title: Hindi named entity recognition using system combination

Authors: Kamal Sarkar

Addresses: Computer Science and Engineering Department, Jadavpur University, Kolkata – 700-032, India

Abstract: This paper reports development of Hindi named entity recognition (NER) system using various machine learning algorithms and their combination. Three machine learning algorithms namely simple K-nearest neighbour (KNN), weighted K-nearest neighbour and HMM have been used for developing three different Hindi NER systems which are finally combined through majority voting to develop an improved hybrid NER system. We have designed set of novel features which have been used for improving the performances of the KNN-based NER systems. A comparative study among our developed NER models has been made based on some standard data sets. This study reveals that performance of the weighted KNN-based NER system is comparable with that of the HMM-based NER system and performance of the hybrid system is significantly better than each individual base model. Our experimental results also show that performance of the hybrid NER system is better than some existing state-of-the art Hindi NER systems.

Keywords: named entity recognition; NER; K-nearest neighbour; KNN; distance-weighted KNN; hidden Markov model; HMM; memory-based learning; system combination; machine learning; applied pattern recognition; natural language processing; NLP; Indian language; Hindi language.

DOI: 10.1504/IJAPR.2018.090519

International Journal of Applied Pattern Recognition, 2018 Vol.5 No.1, pp.11 - 39

Received: 27 May 2017
Accepted: 22 Dec 2017
Published online: 19 Mar 2018 *

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