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) |
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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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