著者
山西 良典 大泉 順平 西原 陽子 福本 淳一
出版者
日本感性工学会
雑誌
日本感性工学会論文誌 (ISSN:18840833)
巻号頁・発行日
vol.15, no.1, pp.31-37, 2016 (Released:2016-02-26)
参考文献数
16

This paper describes linguistic features of generally unreadable person names, which are defined as “KIRAKIRA names,” and proposes a method to detect KIRAKIRA names based on the features. Through the discussions, the following eight features are founded as the linguistic features of KIRAKIRA names: 1) Too many Kanji characters, 2) Too many syllables, 3) Multiple usage of a common Kanji character, 4) Kanji variants are used, 5) The pronunciation of Kanji is generally unknown, 6) Too many stroke count for Kanji, 7) Mismatching of gender between a person and the name, and 8) The pronunciation of name equals an imported word. Based on the features, KIRAKIRA names are automatically detected by using Support Vector Machine. The experiments to detect KIRAKIRA names were conducted for 10,000 names. The results of the experiments showed 81.79% accuracy, 76.89% precision, and 91.84% recall.
著者
山西 良典 大泉 順平 西原 陽子 福本 淳一
出版者
日本感性工学会
雑誌
日本感性工学会論文誌 (ISSN:18840833)
巻号頁・発行日
pp.TJSKE-D-15-00030, (Released:2015-10-09)
参考文献数
16
被引用文献数
1

This paper describes linguistic features of generally unreadable person names, which are defined as “KIRAKIRA names,” and proposes a method to detect KIRAKIRA names based on the features. Through the discussions, the following eight features are founded as the linguistic features of KIRAKIRA names: 1) Too many Kanji characters, 2) Too many syllables, 3) Multiple usage of a common Kanji character, 4) Kanji variants are used, 5) The pronunciation of Kanji is generally unknown, 6) Too many stroke count for Kanji, 7) Mismatching of gender between a person and the name, and 8) The pronunciation of name equals an imported word. Based on the features, KIRAKIRA names are automatically detected by using Support Vector Machine. The experiments to detect KIRAKIRA names were conducted for 10,000 names. The results of the experiments showed 81.79% accuracy, 76.89% precision, and 91.84% recall.
著者
山西 良典 大泉 順平 西原 陽子 福本 淳一
出版者
日本感性工学会
雑誌
日本感性工学会論文誌 (ISSN:18840833)
巻号頁・発行日
vol.15, no.1, pp.31-37, 2016

This paper describes linguistic features of generally unreadable person names, which are defined as "<I>KIRAKIRA</I> names," and proposes a method to detect <I>KIRAKIRA</I> names based on the features. Through the discussions, the following eight features are founded as the linguistic features of <I>KIRAKIRA</I> names: 1) Too many Kanji characters, 2) Too many syllables, 3) Multiple usage of a common Kanji character, 4) Kanji variants are used, 5) The pronunciation of Kanji is generally unknown, 6) Too many stroke count for Kanji, 7) Mismatching of gender between a person and the name, and 8) The pronunciation of name equals an imported word. Based on the features, <I>KIRAKIRA</I> names are automatically detected by using Support Vector Machine. The experiments to detect <I>KIRAKIRA</I> names were conducted for 10,000 names. The results of the experiments showed 81.79% accuracy, 76.89% precision, and 91.84% recall.
著者
山西 良典 大泉 順平 西原 陽子 福本 淳一
出版者
Japan Society of Kansei Engineering
雑誌
日本感性工学会論文誌 (ISSN:18840833)
巻号頁・発行日
2015

This paper describes linguistic features of generally unreadable person names, which are defined as "<I>KIRAKIRA</I> names," and proposes a method to detect <I>KIRAKIRA</I> names based on the features. Through the discussions, the following eight features are founded as the linguistic features of <I>KIRAKIRA</I> names: 1) Too many Kanji characters, 2) Too many syllables, 3) Multiple usage of a common Kanji character, 4) Kanji variants are used, 5) The pronunciation of Kanji is generally unknown, 6) Too many stroke count for Kanji, 7) Mismatching of gender between a person and the name, and 8) The pronunciation of name equals an imported word. Based on the features, <I>KIRAKIRA</I> names are automatically detected by using Support Vector Machine. The experiments to detect <I>KIRAKIRA</I> names were conducted for 10,000 names. The results of the experiments showed 81.79% accuracy, 76.89% precision, and 91.84% recall.