| Presentation | 2014-06-14 Trouble information extraction based on rules and machine learning from Twitter Kohei KURIHARA, Kazutaka SHIMADA, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this paper, we propose a method for extracting trouble information from Twitter. One useful approach is based on machine learning techniques such as SVMs. However, trouble information is a fraction of a percent of all tweets on Twitter. In general, imbalanced training data generates a weak classifier on machine learning techniques. Therefore, we utilize another approach; a rule based method. We consider the advantages and disadvantages of the two approaches through experiments. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Infomation extraction / Trouble infomation / Twitter |
| Paper # | NLC2014-1 |
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| Committee | NLC |
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| Conference Date | 2014/6/7(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) | Trouble information extraction based on rules and machine learning from Twitter |
| Sub Title (in English) | |
| Keyword(1) | Infomation extraction |
| Keyword(2) | Trouble infomation |
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| 1st Author's Name | Kohei KURIHARA |
| 1st Author's Affiliation | Kyushu Institute of Technology() |
| 2nd Author's Name | Kazutaka SHIMADA |
| 2nd Author's Affiliation | Kyushu Institute of Technology |
| Date | 2014-06-14 |
| Paper # | NLC2014-1 |
| Volume (vol) | vol.114 |
| Number (no) | 81 |
| Page | pp.pp.- |
| #Pages | 6 |
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