| Presentation | 2019-11-28 Effectiveness of feature selection as pre-processing of LSTM using W2V Shiori Koga, Tsunenori Mine, Sachio Hirokawa, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Among RNNs, especially LSTM is capable of long-term memory, and can be expected to acquire information including better contextual information, and has been used for various problems including classification problems. In such classification problems, various attempts have been made to improve the accuracy of classification by selecting features such as words that contribute to classification from among the words that make up sentences.However, since LSTM attaches importance to the context, no attempt has been made to select words in LSTM input as far as we know.In this study, we show the effectiveness of using Word2Vec as a word expression in an LSTM input sentence and performing feature selection as a pre-processing of the LSTM input. The results show that the accuracy of classification improves when feature selection is performed. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | text classification / LSTM / feature selection / Word2Vec |
| Paper # | AI2019-34 |
| Date of Issue | 2019-11-21 (AI) |
| Conference Information | |
| Committee | AI |
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| Conference Date | 2019/11/28(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Naoki Fukuta(Shizuoka Univ.) |
| Vice Chair | Yuichi Sei(Univ. of Electro-Comm.) / Yuko Sakurai(AIST) |
| Secretary | Yuichi Sei(Osaka Univ.) / Yuko Sakurai(Tokyo Univ. of Agriculture and Technology) |
| Assistant | |
| Paper Information | |
| Registration To | Technical Committee on Artificial Intelligence and Knowledge-Based Processing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Effectiveness of feature selection as pre-processing of LSTM using W2V |
| Sub Title (in English) | |
| Keyword(1) | text classification |
| Keyword(2) | LSTM |
| Keyword(3) | feature selection |
| Keyword(4) | Word2Vec |
| 1st Author's Name | Shiori Koga |
| 1st Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 2nd Author's Name | Tsunenori Mine |
| 2nd Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 3rd Author's Name | Sachio Hirokawa |
| 3rd Author's Affiliation | Research Institute for Information Technology, Kyushu University(Kyushu Univ.) |
| Date | 2019-11-28 |
| Paper # | AI2019-34 |
| Volume (vol) | vol.119 |
| Number (no) | AI-317 |
| Page | pp.pp.25-30(AI), |
| #Pages | 6 |
| Date of Issue | 2019-11-21 (AI) |