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Estimating Reference Scopes of Wikipedia Article Inner-links
https://ipsj.ixsq.nii.ac.jp/records/190376
https://ipsj.ixsq.nii.ac.jp/records/190376097a45c3-6e22-455b-b5ed-fc9c3e6aef2f
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-TOD1102004.pdf (1.8 MB)
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Copyright (c) 2018 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | Trans(1) | |||||||||
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| 公開日 | 2018-07-11 | |||||||||
| タイトル | ||||||||||
| タイトル | Estimating Reference Scopes of Wikipedia Article Inner-links | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Estimating Reference Scopes of Wikipedia Article Inner-links | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [研究論文] Wikipedia, link suggestion, LDA, word embedding, PMI | |||||||||
| 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 | |||||||||
| 著者所属 | ||||||||||
| Waseda University | ||||||||||
| 著者所属 | ||||||||||
| Waseda University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Waseda University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Waseda University | ||||||||||
| 著者名 |
Renzhi, Wang
× Renzhi, Wang
× Mizuho, Iwaihara
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| 著者名(英) |
Renzhi, Wang
× Renzhi, Wang
× Mizuho, Iwaihara
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Wikipedia is the largest online encyclopedia, utilized as machine-knowledgeable and semantic resources. Links within Wikipedia indicate that two linked articles or parts of them are related each other about their topics. Existing link detection methods focus on linking to article titles, because most of links in Wikipedia point to article titles. But there is a number of links in Wikipedia pointing to corresponding specific segments, such as paragraphs, because the whole article is too general and it is hard for readers to obtain the intention of the link. We propose a method to automatically predict whether a link target is a specific segment or the whole article, and evaluate which segment is most relevant. We propose a combination method of Latent Dirichlet Allocation (LDA) and Maximum Likelihood Estimation (MLE) to represent every segment as a vector, and then we obtain similarity of each segment pair. Finally, we utilize variance, standard deviation and other statistical features to produce prediction results. We also apply word embeddings to embed all the segments into a semantic space and calculate cosine similarities between segment pairs. Then we utilize Random Forest to train a classifier to predict link scopes. Evaluations on Wikipedia articles show an ensemble of the proposed features achieved the best results. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.26(2018) (online) ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Wikipedia is the largest online encyclopedia, utilized as machine-knowledgeable and semantic resources. Links within Wikipedia indicate that two linked articles or parts of them are related each other about their topics. Existing link detection methods focus on linking to article titles, because most of links in Wikipedia point to article titles. But there is a number of links in Wikipedia pointing to corresponding specific segments, such as paragraphs, because the whole article is too general and it is hard for readers to obtain the intention of the link. We propose a method to automatically predict whether a link target is a specific segment or the whole article, and evaluate which segment is most relevant. We propose a combination method of Latent Dirichlet Allocation (LDA) and Maximum Likelihood Estimation (MLE) to represent every segment as a vector, and then we obtain similarity of each segment pair. Finally, we utilize variance, standard deviation and other statistical features to produce prediction results. We also apply word embeddings to embed all the segments into a semantic space and calculate cosine similarities between segment pairs. Then we utilize Random Forest to train a classifier to predict link scopes. Evaluations on Wikipedia articles show an ensemble of the proposed features achieved the best results. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.26(2018) (online) ------------------------------ |
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| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AA11464847 | |||||||||
| 書誌情報 |
情報処理学会論文誌データベース（TOD） 巻 11, 号 2, 発行日 2018-07-11 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7799 | |||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
