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“Part of Speech Tagging – A Corpus Based Approach”

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Smart Trends in Information Technology and Computer Communications (SmartCom 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 628))

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Abstract

POS tagging, an ideal way to augment a corpus is an imperative abstraction for text mining. However with an increase in the amount of linguistic errors and distinctive fashion of language ambiguities, the data filtered by POS tagging is noisier. In this paper, probabilistic tagging and tagging based on Markov models are combined to estimate the association probabilities. Based on this combined approach, error estimation model is defined. Comparison study is made on different corpus available in NLTK such as Crubadan, Brown and INSPEC. The results obtained by the proposed methodologies show a drastic increase in the accuracy rate of about 98% when compared to the existing algorithms which shows an average of 96% accurate. The performance measure is plotted to calculate the error ratio across the maximum-likelihood estimation.

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Authors and Affiliations

  1. Department of Computer Science and Applications, Bangalore University, Bangalore, 560056, India

    S. Rashmi & M. Hanumanthappa

Authors
  1. S. Rashmi
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  2. M. Hanumanthappa
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Corresponding author

Correspondence to S. Rashmi .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Aynur Unal

  2. IT Buzz Limited, Dagenham, UK

    Malaya Nayak

  3. Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

  4. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

  5. Sabar Institute of Technology, Sabarkantha, India

    Amit Joshi

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© 2016 Springer Nature Singapore Pte Ltd.

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Rashmi, S., Hanumanthappa, M. (2016). “Part of Speech Tagging – A Corpus Based Approach”. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_11

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  • DOI: https://doi.org/10.1007/978-981-10-3433-6_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3432-9

  • Online ISBN: 978-981-10-3433-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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