Abstract
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.
- Abney, S., Schapire, R. E., and Singer, Y. 1999. Boosting applied to tagging and PP attachment. EMNLP/VLC-99.Google Scholar
- Cristianini, N. and Shawe-Taylor, J. 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press. Google Scholar
- Eskin, E. 2000. Detecting errors within a corpus using anomaly detection. NAACL-2000. Google Scholar
- Fukunaga, K. 1972. Introduction to Statistical Pattern Recognition. Academic Press. Google Scholar
- Kume, M., Toyoshima, T., and Nagata, M. 1990. Japanese aspect processing for spoken language translation. In Information Processing Society of Japan, the 40th National Convention, 1F-7. 415--416. (In Japanese).Google Scholar
- Murata, M., Ma, Q., Uchimoto, K., and Isahara, H. 1999. An example-based approach to Japanese-to-English translation of tense, aspect, and modality. In TMI '99. 66--76.Google Scholar
- Murata, M., Uchimoto, K., Ma, Q., and Isahara, H. 2001. Using a support-vector machine for Japanese-to-English translation of tense, aspect, and modality. In Proceedings of the ACL Workshop on the Data-Driven Machine Translation. ACM Press, New York. Google Scholar
- Murata, M., Ma, Q., and Isahara, H. 2002. Comparison of three machine-learning methods for Thai part-of-speech tagging. ACM Trans. Asian Language Information Processing 1, 2 (2002), 145--158. Google Scholar
- Nagao, M. 1984. A framework of a mechanical translation between Japanese and English by analogy principle. Artificial and Human Intelligence. 173--180. Google Scholar
- Pietra, S. D., Pietra, V. D., and Lafferty, J. 1995. Inducing features of random fields. Tech. Rep., CMU-CS-95-144. Carnegie Mellon University. Google Scholar
- Ristad, E. S. 1997. Maximum entropy modeling for natural language. ACL/EACL Tutorial Program, Madrid.Google Scholar
- Ristad, E. S. 1998. Maximum entropy modeling toolkit. Release 1.6 beta. http://www.mnemonic.com/software/memt.Google Scholar
- Rivest, R. L. 1987. Learning decision lists. Machine Learning 2 (1987), 229--246. Google Scholar
- Sato, S. 1993. Example-based translation of technical terms. In TMI-93. 58--68.Google Scholar
- Shimizu, M. and Narita, N., eds. 1976. The KODANSHA Japanese-English Dictionary. Kodansha. (In Japanese).Google Scholar
- Shirai, S., Yokoo, A., and Bond, F. 1990. Generation of tense in newspaper translation. In Proceedings of The Institute of Electronics, Information and Communication Engineers, Autumn Convention. D-69. (In Japanese).Google Scholar
- Sumita, E. 1992. Example-based transfer of Japanese adnominal particles into English. IEICE Trans Information and Systems (1992), E75-D(4).Google Scholar
- Yarowsky, D. 1994. Decision lists for lexical ambiguity resolution: Application to accent restoration in Spanish and French. In Proceedings of the 32rd Annual Meeting of the Association of the Computational Linguistics. 88--95. Google Scholar
Index Terms
- Correction of errors in a verb modality corpus for machine translation with a machine-learning method
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