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Finding Co-occurring Topics in Wikipedia Article Segments
https://ipsj.ixsq.nii.ac.jp/records/102426
https://ipsj.ixsq.nii.ac.jp/records/102426412353e4-5b6a-42c5-a72c-3abd9f3151aa
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-IFAT14115008.pdf (819.5 kB)
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Copyright (c) 2014 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | SIG Technical Reports(1) | |||||||
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| 公開日 | 2014-07-25 | |||||||
| タイトル | ||||||||
| タイトル | Finding Co-occurring Topics in Wikipedia Article Segments | |||||||
| タイトル | ||||||||
| 言語 | en | |||||||
| タイトル | Finding Co-occurring Topics in Wikipedia Article Segments | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | トピック抽出・効率化 | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||
| 資源タイプ | technical report | |||||||
| 著者所属 | ||||||||
| Graduate School of Information, Production and Systems Waseda University | ||||||||
| 著者所属 | ||||||||
| Graduate School of Information, Production and Systems Waseda University | ||||||||
| 著者所属 | ||||||||
| Graduate School of Information, Production and Systems Waseda University | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Graduate School of Information, Production and Systems Waseda University | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Graduate School of Information, Production and Systems Waseda University | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Graduate School of Information, Production and Systems Waseda University | ||||||||
| 著者名 |
Renzhi, Wang
Jianmin, Wu
Mizuho, Iwaihara
× Renzhi, Wang Jianmin, Wu Mizuho, Iwaihara
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| 著者名(英) |
Renzhi, Wang
Jianmin, Wu
Mizuho, Iwaihara
× Renzhi, Wang Jianmin, Wu 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, in which articles form knowledgeable and semantic resources. A number of researches about detecting topics and semantic similarity analysis are based on the Wikipedia corpus. Identical topics in different articles indicate that the articles are related to each other about topics. Finding such co-occurring topics is useful to improve the accuracy of querying and clustering, and also to contrast related articles. Existing topic alignment work and topic relevance detection are based on term occurrence. In our research, we discuss incorporating latent topics existing in article segments by utilizing Latent Dirichlet Allocation (LDA), to detect topic relevance. We also study how segment proximities, arising from segment ordering and hyperlinks, shall be incorporated into topic detection and alignment. | |||||||
| 論文抄録(英) | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | Wikipedia is the largest online encyclopedia, in which articles form knowledgeable and semantic resources. A number of researches about detecting topics and semantic similarity analysis are based on the Wikipedia corpus. Identical topics in different articles indicate that the articles are related to each other about topics. Finding such co-occurring topics is useful to improve the accuracy of querying and clustering, and also to contrast related articles. Existing topic alignment work and topic relevance detection are based on term occurrence. In our research, we discuss incorporating latent topics existing in article segments by utilizing Latent Dirichlet Allocation (LDA), to detect topic relevance. We also study how segment proximities, arising from segment ordering and hyperlinks, shall be incorporated into topic detection and alignment. | |||||||
| 書誌レコードID | ||||||||
| 収録物識別子タイプ | NCID | |||||||
| 収録物識別子 | AN10114171 | |||||||
| 書誌情報 |
研究報告情報基礎とアクセス技術（IFAT） 巻 2014-IFAT-115, 号 8, p. 1-6, 発行日 2014-07-25 |
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| Notice | ||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||
| 出版者 | ||||||||
| 言語 | ja | |||||||
| 出版者 | 情報処理学会 | |||||||
