| Presentation | 2018-06-01 Readability Categorization of Japan EIKEN Document using Machine Learning with TF-IDF Rupasingha Arachchilage Hiruni Madhusha Rupasingha, Takeda Yui, Incheon Paik, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Understanding of the readability level and improvements of the text are needed for a specific audience. Accordingly, automatic measurement of text readability has been important issue and there have been many approaches to solve it. Classical readability is measured by readability formulas, and more recent research have employed machine learning algorithms. However, those machine learning approaches use complex features such as linguistic and grammatical features to hinder calculating correct readability score. In this paper, we investigate which features available as input to machine learning improves the performance more easily and accurately. We experiment the readability categorization by three kinds of feature vectors: readability score, Term Frequency-Inverse Document Frequency (TF-IDF), and the combination of them. The classification results by TF-IDF only give accurate results than other two features. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Readability / Eiken Document / Document Classification / Machine Learning |
| Paper # | SC2018-7 |
| Date of Issue | 2018-05-25 (SC) |
| Conference Information | |
| Committee | SC |
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| Conference Date | 2018/6/1(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | UBIC 3D Theater, University of Aizu |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Service Computing for the 4th Industrial Revolution and Other Issues |
| Chair | Masahide Nakamura(Kobe Univ.) |
| Vice Chair | Shinji Kikuchi(Univ. of Aizu) / Yoji Yamato(NTT) |
| Secretary | Shinji Kikuchi(NEC) / Yoji Yamato(Fujitsu Lab.) |
| Assistant | |
| Paper Information | |
| Registration To | Technical Committee on Service Computing |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Readability Categorization of Japan EIKEN Document using Machine Learning with TF-IDF |
| Sub Title (in English) | |
| Keyword(1) | Readability |
| Keyword(2) | Eiken Document |
| Keyword(3) | Document Classification |
| Keyword(4) | Machine Learning |
| 1st Author's Name | Rupasingha Arachchilage Hiruni Madhusha Rupasingha |
| 1st Author's Affiliation | University of Aizu(UoA) |
| 2nd Author's Name | Takeda Yui |
| 2nd Author's Affiliation | University of Aizu(UoA) |
| 3rd Author's Name | Incheon Paik |
| 3rd Author's Affiliation | University of Aizu(UoA) |
| Date | 2018-06-01 |
| Paper # | SC2018-7 |
| Volume (vol) | vol.118 |
| Number (no) | SC-72 |
| Page | pp.pp.37-42(SC), |
| #Pages | 6 |
| Date of Issue | 2018-05-25 (SC) |