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首页> 外文期刊>International journal of human-computer studies >Semantic models and corpora choice when using Semantic Fields to predict eye movement on web pages
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Semantic models and corpora choice when using Semantic Fields to predict eye movement on web pages

机译：使用语义字段预测网页上的眼睛运动时的语义模型和语料库选择

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摘要

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.
机译：比较了十个模型的预测眼动数据的能力，这些数据是从总共1809个网页上的49个目标导向的搜索任务中收集的。作为这六个模型的基础，比较了三种语义模型和两种语料库类型作为语义字段模型的组成部分（Stone和Dennis，2007），该模型估计网页上显示的不同区域的语义显着性。使用潜在语义分析，稀疏非负矩阵分解和向量空间来生成目标和网页文本在语义字段模型的语义成分中的相似性比较。总体而言，Vectorspace是本研究中表现最好的语义模型。使用两种类型的语料库或知识库来告知语义模型，即众所周知的TASA语料库和其他从Wikipedia百科全书构建的语料库。在所有情况下，Wikipedia语料库都优于TASA语料库。基于非语料库的语义字段模型在这些任务上的表现较差，该模型包含单词重叠。还包括三个基线模型作为评估语义场模型有效性的比较点。在预测参与者的眼动数据时，基于语料库的语义场模型在所有情况下都优于基线模型。最终目标页面和学生数据（扩张）均表明参与者正在积极执行面向目标的搜索任务。

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