Creating Noisy Parallel Corpus from Newswire Articles Using Cross - Language Information Retrieval

• Item type: Journal(1)
• 公開日: 1999-01-15
• タイトル: Creating Noisy Parallel Corpus from Newswire Articles Using Cross - Language Information Retrieval
• タイトル / 言語: en
• タイトル: Creating Noisy Parallel Corpus from Newswire Articles Using Cross - Language Information Retrieval
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 論文
• 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
• その他タイトル / その他のタイトル: 自然言語処理
• 著者所属: Communication and Information System's Laboratory Research and Development Center Toshiba Corporation/Presently with Department of Information Science Faculty of Science University of Tokyo
• 著者所属: Communication and Information System's Laboratory Research and Development Center Toshiba Corporation
• 著者所属: Communication and Information System's Laboratory Research and Development Center Toshiba Corporation
• 著者所属(英): en
• 著者所属(英): Communication and Information System's Laboratory Research and Development Center, Toshiba Corporation/Presently with Department of Information Science, Faculty of Science, University of Tokyo
• 著者所属(英): en
• 著者所属(英): Communication and Information System's Laboratory Research and Development Center, Toshiba Corporation
• 著者所属(英): en
• 著者所属(英): Communication and Information System's Laboratory Research and Development Center, Toshiba Corporation
• 著者名: Nigel, Collier; Hideki, Hirakawa; Akira, Kumano
• 著者名(英): Nigel, Collier; Hideki, Hirakawa; Akira, Kumano
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In this paper we present an adaptation of cross-language information retrieval for the production of an aligned bilingual corpus from noisy-parallel English-Japanese newswire articles. We implement the standard vector space model and show though simulation the effectiveness of five variations for the alignment task. The methods are computationally efficient easy to evaluate and generalizable to other genres and language pairs-an important factor if we are to use the aligned articles for knowledge acquisition in unrestricted domains. Our results show that alignment precision levels of over 70% at 70% recall are possible.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In this paper we present an adaptation of cross-language information retrieval for the production of an aligned bilingual corpus from noisy-parallel English-Japanese newswire articles. We implement the standard vector space model and show though simulation the effectiveness of five variations for the alignment task. The methods are computationally efficient, easy to evaluate, and generalizable to other genres and language pairs-an important factor if we are to use the aligned articles for knowledge acquisition in unrestricted domains. Our results show that alignment precision levels of over 70% at 70% recall are possible.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 40, 号 1, p. 351-361, 発行日 1999-01-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764