| Presentation | 2010-06-25 Automatic Extraction of Human Activity Attributes from Twitter THE Nguyen MINH, Takahiro KAWAMURA, Yasuyuki TAHARA, Akihiko OHSUGA, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The goal of this paper is to describe a method to automatically extract all basic attributes namely actor, action, object, time and location which belong to an activity, in each sentence retrieved from Twitter. Previous work had some limitations, such as inability of extracting infrequent activities, high setup cost, inability of extracting all attributes. To resolve these problems, this paper proposes a novel approach that treats the activity extraction as a sequence labeling problem, and automatically makes its own training data. This approach can extract infrequent activities, and has advantages such as domain-independence, scalability, and unnecessary hand-tagged data. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Human Activity / Twitter / Semantic Network / Web Mining / Self-Supervised Learning |
| Paper # | AI2010-4 |
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| Committee | AI |
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| Conference Date | 2010/6/18(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 | Artificial Intelligence and Knowledge-Based Processing (AI) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Automatic Extraction of Human Activity Attributes from Twitter |
| Sub Title (in English) | |
| Keyword(1) | Human Activity |
| Keyword(2) | |
| Keyword(3) | Semantic Network |
| Keyword(4) | Web Mining |
| Keyword(5) | Self-Supervised Learning |
| 1st Author's Name | THE Nguyen MINH |
| 1st Author's Affiliation | () |
| 2nd Author's Name | Takahiro KAWAMURA |
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| 3rd Author's Name | Yasuyuki TAHARA |
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| 4th Author's Name | Akihiko OHSUGA |
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| Date | 2010-06-25 |
| Paper # | AI2010-4 |
| Volume (vol) | vol.110 |
| Number (no) | 105 |
| Page | pp.pp.- |
| #Pages | 5 |
| Date of Issue | |