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An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature
https://tokushima-u.repo.nii.ac.jp/records/2000075
https://tokushima-u.repo.nii.ac.jp/records/20000758611fbd9-9e5b-4723-9d07-ec15fd2e6dc9
| 名前 / ファイル | ライセンス | アクション |
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pone_9_7_e102039.pdf (324 KB)
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| Item type | 文献 / Documents(1) | |||||||||||||||
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| 公開日 | 2024-09-05 | |||||||||||||||
| アクセス権 | ||||||||||||||||
| アクセス権 | open access | |||||||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||||||
| 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 | |||||||||||||||
| 出版社版DOI | ||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||
| 関連識別子 | https://doi.org/10.1371/journal.pone.0102039 | |||||||||||||||
| 言語 | ja | |||||||||||||||
| 関連名称 | 10.1371/journal.pone.0102039 | |||||||||||||||
| 出版タイプ | ||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||
| タイトル | ||||||||||||||||
| タイトル | An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature | |||||||||||||||
| 言語 | en | |||||||||||||||
| タイトル別表記 | ||||||||||||||||
| その他のタイトル | Unsupervised Biomedical Relation Extraction | |||||||||||||||
| 言語 | en | |||||||||||||||
| 著者 |
Quan, Changqin
× Quan, Changqin
× Wang, Meng
× 任, 福継
WEKO
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| 抄録 | ||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||
| 内容記述 | The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1) Protein–protein interactions extraction, and (2) Gene–suicide association extraction. The evaluation of task (1) on the benchmark dataset (AImed corpus) showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene–suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method. | |||||||||||||||
| 言語 | en | |||||||||||||||
| 書誌情報 |
en : PLOS ONE 巻 9, 号 7, p. e102039, 発行日 2014-07-18 |
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| 収録物ID | ||||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||||
| 収録物識別子 | 19326203 | |||||||||||||||
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| 出版者 | PLOS | |||||||||||||||
| 言語 | en | |||||||||||||||
| 権利情報 | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 権利情報 | This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |||||||||||||||
| EID | ||||||||||||||||
| 識別子 | 287583 | |||||||||||||||
| 識別子タイプ | URI | |||||||||||||||
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| 言語 | eng | |||||||||||||||
