Generating Heatmap for Unknown Documents towards Readability Measurement
Pages 47 - 48
Abstract
The key idea behind this paper is to generate fixation heatmap of unknown documents to visualize and determine the focus areas in a document as a first step towards the readability measurement of the document. The data samples were collected by conducting experiment with nine participants reading 15 documents and the proposed method was to predict the fixation duration of each word in the documents. A Random Forest Regression model was used to predict the fixation duration per word and we achieved a mean regression score (R2) of 0.757 for all the documents.
References
[1]
Ralf Biedert, Andreas Dengel, Mostafa Elshamy, and Georg Buscher. 2012. Towards robust gaze-based objective quality measures for text. In Proceedings of the Symposium on Eye Tracking Research and Applications. 201--204.
[2]
John Hale. 2001. A probabilistic Earley parser as a psycholinguistic model. In Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies. Association for Computational Linguistics, 1--8.
[3]
Shoya Ishimaru, Syed Saqib Bukhari, Carina Heisel, Jochen Kuhn, and Andreas Dengel. 2016. Towards an intelligent textbook: eye gaze based attention extraction on materials for learning and instruction in physics. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. 1041--1045.
[4]
Brian Roark. 2001. Probabilistic top-down parsing and language modeling. Computational linguistics 27, 2 (2001), 249--276.
Index Terms
- Generating Heatmap for Unknown Documents towards Readability Measurement
Index terms have been assigned to the content through auto-classification.
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Published In
March 2020
153 pages
ISBN:9781450375139
DOI:10.1145/3379336
Copyright © 2020 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGCHI: Specialist Interest Group in Computer-Human Interaction of the ACM
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 March 2020
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IUI '20
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- SIGCHI
IUI '20: 25th International Conference on Intelligent User Interfaces
March 17 - 20, 2020
Cagliari, Italy
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