Paper Info
Title
Synthesizing image representations of linguistic and topological features for predicting areas of attention
Abstract
Depending on the reading objective or task, text portions with certain linguistic features require more user attention to maximize the level of understanding. The goal is to build a predictor of these text areas. Our strategy consists in synthesizing image representations of linguistic features, that allows us to use natural language processing techniques while preserving the topology of the text. Eye-tracking technology allows us to precisely observe the identity of fixated words on a screen and their fixation duration. Then, we estimate the scaling factors of a linear combination of image representations of linguistic features that best explain certain gaze evidence, which leads us to a quantification of the influence of linguistic features in reading behavior. Finally, we can compute saliency maps that contain a prediction of the most interesting or cognitive demanding areas along the text. We achieve an important prediction accuracy of the text areas that require more attention for users to maximize their understanding in certain reading tasks, suggesting that linguistic features are good signals for prediction.
Year
DOI
Venue
2012
10.1007/978-3-642-32695-0_29
PRICAI
Keywords
Field
DocType
linguistic feature,user attention,text portion,important prediction accuracy,reading objective,synthesizing image representation,topological feature,certain linguistic feature,certain reading task,image representation,text area
Reading strategy,Linear combination,Computer science,Salience (neuroscience),Machine translation,Image representation,Artificial intelligence,Natural language processing,Cognition,Topology,Gaze,Linguistics,Machine learning
Conference
Citations 
PageRank 
References 
3
0.42
10
Authors
6
Name
Order
Citations
PageRank
Pascual Martínez-Gómez1617.36
Tadayoshi Hara21189.54
Chen Chen330.42
Kyohei Tomita430.42
Yoshinobu Kano542430.19
Akiko N. Aizawa6678120.63