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Automatic Correction of Errors in Annotated Corpus Using Kernel Ripple-Down Rules
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 6,  2016, pp.636-644
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.6.636
 Title & Authors
Automatic Correction of Errors in Annotated Corpus Using Kernel Ripple-Down Rules
Park, Tae-Ho; Cha, Jeong-Won;
Annotated Corpus is important to understand natural language using machine learning method. In this paper, we propose a new method to automate error reduction of annotated corpora. We use the Ripple-Down Rules(RDR) for reducing errors and Kernel to extend RDR for NLP. We applied our system to the Korean Wikipedia and blog corpus errors to find the annotated corpora error type. Experimental results with various views from the Korean Wikipedia and blog are reported to evaluate the effectiveness and efficiency of our proposed approach. The proposed approach can be used to reduce errors of large corpora.
morphological annotation corpora;error correction;kernel RDR;natural language processing;
 Cited by
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