Semantic models and corpora choice when using Semantic Fields to predict eye movement on web pages

Ten models are compared in their ability to predict eye-tracking data that was collected from 49 participants' goal-oriented search tasks on a total of 1809 Web pages. Forming the basis of six of these models, three semantic models and two corpus types are compared as components for the Semantic Fields model (Stone and Dennis, 2007) that estimates the semantic salience of different areas displayed on Web pages. Latent Semantic Analysis, Sparse Nonnegative Matrix Factorization, and Vectorspace were used to generate similarity comparisons of goal and Web page text in the semantic component of the Semantic Fields model. Overall, Vectorspace was the best performing semantic model in this study. Two types of corpora or knowledge-bases were used to inform the semantic models, the well known TASA corpus and other corpora that were constructed from the Wikipedia encyclopedia. In all cases the Wikipedia corpora outperformed the TASA corpora. A non-corpus-based Semantic Fields model that incorporated word overlap performed more poorly at these tasks. Three baseline models were also included as a point of comparison to evaluate the effectiveness of the Semantic Fields models. In all cases the corpus-based Semantic Fields models outperformed the baseline models when predicting the participants' eye-tracking data. Both final destination pages and pupil data (dilation) indicated that participants' were actively performing goal-oriented search tasks.

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► Semantics Fields models eye-tracking data better than display-based models.
► Overall Vectorspace provides better similarity estimates of text than LSA or SpNMF.
► Target specific Wikipedia corpora outperform generic TASA corpus.