2013 Fiscal Year Final Research Report
Development of a basic technology for verbalizing time-series data for computing with words
| Project/Area Number |
23500274
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Ochanomizu University |
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Principal Investigator |
KOBAYASHI Ichiro お茶の水女子大学, 大学院人間文化創成科学研究科, 教授 (60281440)
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| Co-Investigator(Kenkyū-buntansha) |
IWAZUME Michiaki 独立行政法人情報通信研究機構, 知識創成コミュニケーション研究センター, 研究マネージャー (80319756)
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| Project Period (FY) |
2011 – 2013
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| Keywords | 時系列データ / 言語化 / SAX / 編集距離 / バイグラムモデル / テキスト生成 / 機械学習 / 動画 |
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| Research Abstract |
We aim to develop a method to verbalize multiple time-series data with words. We calculate the correlation coefficient between two time-series data and convert the data into symbols by SAX. The edit distance of the symbols of two time-series data is calculated and expressed with words. We also propose a method to verbalize the interaction between a human and an object. SAX with dynamic programming is applied to the time-series data to extract their patterns, and then the correspondence between the patterns and their semantic labels are learned by a log-linear model. We build a bi-gram model based on the collected linguistic resources. The most likely sentences to explain the interaction are generated by solving the bi-gram model with dynamic programming. Moreover, to enhance the linguistic resources used to generate sentences, we adopt transfer learning of n-gram language models. Through experiments, we have confirmed that our proposed framework works well.
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Research Products
(13 results)
