Japanese / English


論文の言語 英語
著者 Utpal Garain, Onkar Pandit, Olivier Augereau, Ayano Okoso, Koichi Kise
論文名 Identification of Reader Specific Difficult Words by Analyzing Eye Gaze and Document Content
論文誌名 Proceedings of the International Conference on Document Analysis and Recognition
ページ数 6 pages
出版社 IEEE
年月 2017年11月
要約 This paper presents an approach for identifying reader specific difficult words while someone is reading a textual document. The work is motivated by the need of developing human-document interaction systems, in general and creating person-specific online educational content, in particular. Eye gaze information gives person specific behavior whereas textual content is analyzed to get general linguistic aspect of the document content. These two pieces of information are fused together through machine learning algorithms to identify the set of difficult words for a particular reader reading a particular document. An annotated dataset has been created where each word in a document is marked with its bounding box information and each reader identifies a set of difficult words while reading the document. The dataset consists of sixteen documents and each document is read by five subjects. The method is evaluated through recall-precision analysis. The impressive precision at high recall attests the feasibility of building a practical application based on this research. The experiment further brings out several interesting facts about human reading behavior.