| Presentation | 2012-08-31 Feature Words that Describe Problem Sentence in Scientific Article Toshihiko SAKAI, Sachio HIROKAWA, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The literature survey on related work requires a lot of efforts in reading all articles that are retrieved by queries. We have to understand the contents from several view points, such as the problem and the method that the articles describe. Search from these viewpoints will improve the efficiency of survey, if particular segments of articles were extracted, indexed and can be used as auxiliary query. This paper focuses on the sentence that describes the problem in an abstract and the feature sets that describe such problem sentences. Discernment performance are evaluated by 10-fold cross-validation for six candidates of feature sets. It turned out that the set of all words gains the best performance if 90% of the data are used as a training data. However, the set of a small number of words with positive scores outperforms other feature sets, if the training data is only 10%. In such a realistic situation, the feature words are effective in improving discernment. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Feature Words / SVM / Cross validation / Scientific Article |
| Paper # | NLC2012-23 |
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| Conference Information | |
| Committee | NLC |
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| Conference Date | 2012/8/23(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Natural Language Understanding and Models of Communication (NLC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Feature Words that Describe Problem Sentence in Scientific Article |
| Sub Title (in English) | |
| Keyword(1) | Feature Words |
| Keyword(2) | SVM |
| Keyword(3) | Cross validation |
| Keyword(4) | Scientific Article |
| 1st Author's Name | Toshihiko SAKAI |
| 1st Author's Affiliation | Kyushu University() |
| 2nd Author's Name | Sachio HIROKAWA |
| 2nd Author's Affiliation | Kyushu University |
| Date | 2012-08-31 |
| Paper # | NLC2012-23 |
| Volume (vol) | vol.112 |
| Number (no) | 196 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |