2017
DOI: 10.1587/transinf.2016edl8247
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Using Machine Learning for Automatic Estimation of Emphases in Japanese Documents

Abstract: SUMMARYWe propose a method for automatic emphasis estimation using conditional random fields. In our experiments, the value of Fmeasure obtained using our proposed method (0.31) was higher than that obtained using a random emphasis method (0.20), a method using TF-IDF (0.21), and a method based on LexRank (0.26). On the contrary, the value of F-measure of obtained using our proposed method (0.28) was slightly worse as compared with that obtained using manual estimation (0.26-0.40, with an average of 0.35).

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Cited by 4 publications
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“…While picking the best satisfactory technique for your theme, you should inspect various elements. Research limits and moral hardships are instances of issues that could influence the nature of your exploration [18].…”
Section: Methodsmentioning
confidence: 99%
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“…While picking the best satisfactory technique for your theme, you should inspect various elements. Research limits and moral hardships are instances of issues that could influence the nature of your exploration [18].…”
Section: Methodsmentioning
confidence: 99%
“…In the process of Japanese translation, in addition to master a lot of vocabulary and accumulate translation skills, the accumulation of information resources beyond Japanese translation is also negligible. This background is particularly true in proprietary Japanese translation or Japanese online translation [18]. It cannot be finished just rely on translation skills.…”
Section: The Accumulation Of Information Resources Beyond Japanese Tr...mentioning
confidence: 99%