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End-to-End Korean Part-of-Speech Tagging Using Copying Mechanism

Published:14 February 2018Publication History
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Abstract

In this article, we introduce a novel neural architecture for the end-to-end Korean Part-of-Speech (POS) tagging problem. To address the problem, we extend the present recurrent neural network-based sequence-to-sequence models to deal with the key challenges in this task: rare word generation and POS tagging. To overcome these issues, Input-Feeding and Copying mechanism are adopted. Although our approach does not require any manual features or preprocessed pattern matching dictionaries, our best single model achieves an F-score of 97.08. This is competitive with the current state-of-the-art model (F-score 98.03), which requires extensive manual feature processing.

References

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    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 17, Issue 3
      September 2018
      196 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3184403
      Issue’s Table of Contents

      Copyright © 2018 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 14 February 2018
      • Accepted: 1 December 2017
      • Revised: 1 September 2017
      • Received: 1 May 2017
      Published in tallip Volume 17, Issue 3

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