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Analyzing Tagging Accuracy of Part-of-Speech Taggers

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Genetic and Evolutionary Computing (GEC 2015)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 388))

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  • International Conference on Genetic and Evolutionary Computing

Abstract

Automated part-of-speech (POS) tagging has been a very active research area for many years and is the foundation of natural language processing systems. Natural Language Toolkit (NLTK) library in the Python environment provides the necessary tools for tagging, but doesn’t actually tell us what methods work the best. Therefore, this work analyzes the performance of part-of-speech taggers, namely the NLTK Default tagger, Regex tagger and N-gram taggers (Unigram, Bigram and Trigram) on a particular corpus. The corpora we have used for the analysis are; Brown, Penn Treebank and CoNLL2000. We have applied all taggers to these three corpora, resultantly we have shown that whereas Unigram tagger does the best tagging in all corpora, the combination of taggers does better if it is correctly ordered.

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References

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Author information

Authors and Affiliations

  1. University of Computer Studies, Mandalay, Myanmar

    Nyein Pyae Pyae Khin & Than Nwe Aung

Authors
  1. Nyein Pyae Pyae Khin
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  2. Than Nwe Aung
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Corresponding authors

Correspondence to Nyein Pyae Pyae Khin or Than Nwe Aung .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Thi Thi Zin

  2. School of Computer Science and Tech..., Harbin Institute of Technology, Shenzhen Graduate School , Shenzhen, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engg, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  4. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Pyke Tin

  5. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Mitsuhiro Yokota

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© 2016 Springer International Publishing Switzerland

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Khin, N.P.P., Aung, T.N. (2016). Analyzing Tagging Accuracy of Part-of-Speech Taggers. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. GEC 2015. Advances in Intelligent Systems and Computing, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-319-23207-2_35

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  • DOI: https://doi.org/10.1007/978-3-319-23207-2_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23206-5

  • Online ISBN: 978-3-319-23207-2

  • eBook Packages: EngineeringEngineering (R0)

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