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An introduction to support Vector Machines: and other kernel-based learning methods
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Comparison of three machine-learning methods for Thai part-of-speech tagging
ACM Transactions on Asian Language Information Processing (TALIP)
Machine Learning
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Detecting errors within a corpus using anomaly detection
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Using a support-vector machine for Japanese-to-English translation of tense, aspect, and modality
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
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ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
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PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
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ACM Transactions on Information Systems (TOIS)
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In recent years, various types of tagged corpora have been constructed and much research using tagged corpora has been done. However, tagged corpora contain errors, which impedes the progress of research. Therefore, the correction of errors in corpora is an important research issue. In this study we investigate the correction of such errors, which we call corpus correction. Using machine-learning methods, we applied corpus correction to a verb modality corpus for machine translation. We used the maximum-entropy and decision-list methods as machine-learning methods. We compared several kinds of methods for corpus correction in our experiments, and determined which is most effective by using a statistical test. We obtained several noteworthy findings: (1) Precision was almost the same for both detection and correction, so it is more convenient to do both correction and detection, rather than detection only. (2) In general, the maximum-entropy method worked better than the decision-list method; but the two methods had almost the same precision for the top 50 pieces of extracted data when closed data was used. (3) In terms of precision, the use of closed data was better than the use of open data; however, in terms of the total number of extracted errors, the use of open data was better than the use of closed data. Based on our analysis of these results, we developed a good method for corpus correction. We confirmed the effectiveness of our method by carrying out experiments on machine translation. As corpus-based machine translation continues to be developed, the corpus correction we discuss in this article should prove to be increasingly significant.