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アイテム
Enhancing Semantic Correlation between Instances and Relations for Zero-Shot Relation Extraction
http://hdl.handle.net/10061/0002000644
http://hdl.handle.net/10061/00020006442e1ef1f9-bda4-4c43-b50d-3b40d3ccc87e
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-10-25 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Enhancing Semantic Correlation between Instances and Relations for Zero-Shot Relation Extraction | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Information Extraction | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Zero-Shot Learning | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Relation Extraction | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Semantic Correlation | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ | journal article | |||||||||||
| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| 著者 |
Tran, Van-Hien
× Tran, Van-Hien
× 大内, 啓樹× Shindo, Hiroyuki
× Matsumoto, Yuji
× 渡辺, 太郎 |
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Zero-shot relation extraction aims to recognize (new) unseen relations that cannot be observed during training. Due to this point, recognizing unseen relations with no corresponding labeled training instances is a challenging task. Recognizing an unseen relation between two entities in an input instance at the testing time, a model needs to grasp the semantic relationship between the instance and all unseen relations to make a prediction. This study argues that enhancing the semantic correlation between instances and relations is key to effectively solving the zero-shot relation extraction task. A new model entirely devoted to this goal through three main aspects was proposed: learning effective relation representation, designing purposeful mini-batches, and binding two-way semantic consistency. Experimental results on two benchmark datasets demonstrate that our approach significantly improves task performance and achieves state-of-the-art results. Our source code and data are publicly available. | |||||||||||
| 書誌情報 |
en : Journal of Natural Language Processing 巻 30, 号 2, p. 304-329, 発行日 2023-06-15 |
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| 出版者 | ||||||||||||
| 出版者 | The Association for Natural Language Processing | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2185-8314 | |||||||||||
| 出版者版DOI | ||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.5715/jnlp.30.304 | |||||||||||
| 出版者版URI | ||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||
| 識別子タイプ | URI | |||||||||||
| 関連識別子 | https://www.jstage.jst.go.jp/article/jnlp/30/2/30_304/_article/-char/en | |||||||||||
| 権利 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||||
| 権利情報 | $00A9 2023 The Association for Natural Language Processing. Licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). | |||||||||||
| 著者版フラグ | ||||||||||||
| 出版タイプ | NA | |||||||||||
