PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Predicting Text Relevance from Sequential Reading Behavior
Michael Pfeiffer, Amir R. Saffari A. A. and Andreas Juffinger
In: NIPS 2005 Workshop on Machine Learning for Implicit Feedback and User Modeling, 10 Dec 2005, Whistler, Canada.

Abstract

In this paper we show that it is possible to make good predictions of text relevance, from only features of conscious eye movements during reading. We pay special attention to the order in which the lines of text are read, and compute simple features of this sequence. Artificial neural networks are applied to classify the relevance of the read lines. The use of ensemble techniques creates stable predictions and good generalization abilities. Using these methods we won the first competition of the PASCAL Inferring Relevance from Eye Movement Challenge.

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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:First Place in PASCAL Inferring Relevance from Eye Movements Challenge (Competition One)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
ID Code:1506
Deposited By:Michael Pfeiffer
Deposited On:28 November 2005