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Implementation of Word Sense Disambiguation on Hadoop Using Map-Reduce

  • Anuja NairEmail author
  • Kaushik Kyada
  • Neel Zadafiya
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 106)

Abstract

In Natural Language Processing, it is essential to find a correct sense of sentences or document is written for these type of problem is called word sense disambiguation problem. Currently, any machine learning application based on natural language processing requires to solve this type of problem. To identify the correct sense, pywsd (Python implementations of word sense disambiguation technologies) is used, which consists of different lesk algorithms, maximizing similarity tools, superwised WSD, and vector space models. Using simple lesk algorithm of pywsd, WSD is done on a given document and it is established in the Hadoop environment. Implementation on multinode Hadoop environment helps majorly to reduce the complexity of the application. Also, Map-Reduce is a parallel programming environment, which reduces the response time of the implemented application.

Keywords

Word sense disambiguation Lesk algorithm Multinode Hadoop Map-Reduce Pywsd 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Institute of Technology, Nirma UniversityAhmedabadIndia

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