Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments

• Item type: Journal(1)
• 公開日: 2017-01-15
• タイトル: Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments
• タイトル / 言語: en
• タイトル: Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文（テクニカルノート）] eye-tracking, reading, error correction, fixation-to-word mapping
• 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: The University of Tokyo
• 著者所属: National Institute of Informatics
• 著者所属: National Institute of Informatics／The University of Tokyo
• 著者所属(英): en
• 著者所属(英): The University of Tokyo
• 著者所属(英): en
• 著者所属(英): National Institute of Informatics
• 著者所属(英): en
• 著者所属(英): National Institute of Informatics / The University of Tokyo
• 著者名: Akito, Yamaya; Goran, Topić; Akiko, Aizawa
• 著者名(英): Akito, Yamaya; Goran, Topić; Akiko, Aizawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In eye-tracking-based reading behavior research, gaze sampling errors often negatively affect gaze-to-word mapping. In this paper, we propose a method for more accurate mapping by first taking adjacent horizontally progressive fixations as segments, and then classifying the segments into six classes using a random forest classifier. The segments are then reconstructed based on the classification, and are associated with a document line using a dynamic programming algorithm. The combination of segment-to-line mapping and transition classification achieved 87% mapping accuracy. We also witnessed a reduction of manual annotation time when the mapping was used as an annotation guiding tool.; ------------------------------; 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.25(2017) (online); DOI http://dx.doi.org/10.2197/ipsjjip.25.100; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In eye-tracking-based reading behavior research, gaze sampling errors often negatively affect gaze-to-word mapping. In this paper, we propose a method for more accurate mapping by first taking adjacent horizontally progressive fixations as segments, and then classifying the segments into six classes using a random forest classifier. The segments are then reconstructed based on the classification, and are associated with a document line using a dynamic programming algorithm. The combination of segment-to-line mapping and transition classification achieved 87% mapping accuracy. We also witnessed a reduction of manual annotation time when the mapping was used as an annotation guiding tool.; ------------------------------; 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.25(2017) (online); DOI http://dx.doi.org/10.2197/ipsjjip.25.100; ------------------------------
• 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 58, 号 1, 発行日 2017-01-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764