Abstract
Text rewriting pattern mining was important for stylistic change detection and machine (aided) writing. This paper combined monolingual sentence alignment and monolingual word alignment for text rewriting pattern mining. Edit distance was used to compute sentence similarity for sentence alignment, and a log-linear modification of IBM Model 2 was used for word alignment. We built a rewriting corpus of Jin Yong’s novels, on which quantitative and qualitative experiments were carried out. Rewriting patterns were extracted and classified, including function word usages and some content word usages, which reflected the stylistic shift of the author.
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Acknowledgments
We thank Qian Wang and Yue Zhang for their helpful discussions on literary computing. We are grateful to the anonymous reviewers for constructive advices to improve this paper. This work is partially supported by grants from the National Natural Science Foundation of China (No. 61402419) and the National Social Science Foundation of China (No. 14BYY096).
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Jia, Y., Wang, L., Zan, H. (2018). Text Rewriting Pattern Mining Based on Monolingual Alignment. In: Hong, JF., Su, Q., Wu, JS. (eds) Chinese Lexical Semantics. CLSW 2018. Lecture Notes in Computer Science(), vol 11173. Springer, Cham. https://doi.org/10.1007/978-3-030-04015-4_47
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DOI: https://doi.org/10.1007/978-3-030-04015-4_47
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