WEKO3
アイテム
Sarcasm Detection Using Role Pairs: Bootstrapping Role Pairs and Weighting
http://hdl.handle.net/10228/00008094
http://hdl.handle.net/10228/00008094a825468f-1a74-4082-b1ff-098a5e9893df
| 名前 / ファイル | ライセンス | アクション |
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IALP51396.2020.9310499.pdf (1.3 MB)
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| Item type | 学術雑誌論文 = Journal Article(1) | |||||||||||||
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| 公開日 | 2021-03-23 | |||||||||||||
| 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 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Sarcasm Detection Using Role Pairs: Bootstrapping Role Pairs and Weighting | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 著者 |
Hiai, Satoshi
× Hiai, Satoshi× 嶋田, 和孝
WEKO
13734
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Sarcasm detection has been treated as a task that classifies text as sarcastic or non-sarcastic. Sarcasm detection is a significant challenge for sentiment analysis because sarcasm involves a positive expression with negative meaning. Previous work focused on relation information between pairs of role expressions, such as “boss and staff,” and proposed a sarcasm detection method based on surface and relation information. Although they showed the effectiveness of the relation information, due to the small scale of the list of role pairs, the effectiveness was limited to a small scale. In this paper, we attempt to improve the performance of the sarcasm detection method. We propose a role pair extraction method using a bootstrap method to obtain a larger role pair list than the previous work. In addition, we propose weighting methods for each role pair. Then, we evaluate the method using our role pair list and weighting methods by comparing the method using the list of the previous work. We find that we can obtain approximately threefold increase for role pairs. Although the effectiveness is limited, the experimental result shows a potential benefit because the method with topic similarity can reduce the influence of such noise pairs. | |||||||||||||
| 備考 | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | International Conference on Asian Language Processing (IALP 2020), 4-6 December, 2020, Kuala Lumpur, Malaysia（新型コロナ感染拡大に伴い、オンライン開催に変更） | |||||||||||||
| 書誌情報 |
2020 International Conference on Asian Language Processing (IALP) p. 235-240, 発行日 2020-12-04 |
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| 出版社 | ||||||||||||||
| 出版者 | IEEE | |||||||||||||
| DOI | ||||||||||||||
| 関連タイプ | isVersionOf | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.1109/IALP51396.2020.9310499 | |||||||||||||
| ISBN | ||||||||||||||
| 識別子タイプ | ISBN | |||||||||||||
| 関連識別子 | 978-1-7281-7689-5 | |||||||||||||
| ISBN | ||||||||||||||
| 識別子タイプ | ISBN | |||||||||||||
| 関連識別子 | 978-1-7281-7690-1 | |||||||||||||
| 日本十進分類法 | ||||||||||||||
| 主題Scheme | NDC | |||||||||||||
| 主題 | 548 | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報 | Copyright (c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Sarcasm | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Relation representation | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Sentiment analysis | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Opinion mining | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Microblogging | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | AM | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||
| 査読の有無 | ||||||||||||||
| 値 | yes | |||||||||||||
| 研究者情報 | ||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/196_ja.html | |||||||||||||
| 論文ID（連携） | ||||||||||||||
| 値 | 10361978 | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 8622 | |||||||||||||
