| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2019-05-10 |
| タイトル |
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|
タイトル |
条件付確率場と自己教師あり学習を用いた行動属性の自動抽出と評価 |
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言語 |
ja |
| タイトル |
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|
タイトル |
Automatic Extraction and Evaluation of Human Activity Using Conditional Random Fields and Self-Supervised Learning |
|
言語 |
en |
| 言語 |
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|
言語 |
jpn |
| キーワード |
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|
言語 |
en |
|
主題 |
Real-world Agent |
| キーワード |
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|
言語 |
en |
|
主題 |
Human Activity |
| キーワード |
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|
言語 |
en |
|
主題 |
Web Mining |
| キーワード |
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|
言語 |
en |
|
主題 |
Semantic Network |
| キーワード |
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|
言語 |
en |
|
主題 |
Self-Supervised Learning |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 著者 |
グエン, ミン テイ
川村, 隆浩
中川, 博之
田原, 康之
大須賀, 昭彦
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
In our definition, human activity can be expressed by five basic attributes: actor, action, object, time and location. The goal of this paper is describe a method to automatically extract all of the basic attributes and the transition between activities derived from sentences in Japanese web pages. However, previous work had some limitations, such as high setup costs, inability to extract all attributes, limitation on the types of sentences that can be handled, and insufficient consideration interdependency among attributes. To resolve these problems, this paper proposes a novel approach that uses conditional random fields and self-supervised learning. Given a small corpus sample as input, it automatically makes its own training data and a feature model. Based on the feature model, it automatically extracts all of the attributes and the transition between the activities in each sentence retrieved from the Web corpus. This approach treats activity extraction as a sequence labeling problem, and has advantages such as domain-independence, scalability, and does not require any human input. Since it is unnecessary to fix the number of elements in a tuple, this approach can extract all of the basic attributes and the transition between activities by making only a single pass. Additionally, by converting to simpler sentences, the approach can deal with complex sentences retrieved from the Web. In an experiment, this approach achieves high precision (activity: 88.9%, attributes: over 90%, transition: 87.5%). |
| 書誌情報 |
ja : 人工知能学会論文誌
en : Transactions of the Japanese Society for Artificial Intelligence
巻 26,
号 1,
p. 166-178,
発行日 2011
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| 出版者 |
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出版者 |
人工知能学会 |
| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
1346-8030 |
| DOI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
DOI |
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関連識別子 |
10.1527/tjsai.26.166 |
| 権利 |
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|
権利情報 |
Copyright © 人工知能学会 2011 |
| 関連サイト |
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1527/tjsai.26.166 |
| 著者版フラグ |
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|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
26_166 (564.6 kB)
