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Identifying and Pruning Features for Classifying Translated and Post-edited Gaze Durations

机译：识别和修剪功能，用于对翻译后和编辑后的凝视时间进行分类

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摘要

The present paper reports on various experiments carried out to classify the source and target gaze fixation durations on an eye tracking dataset, namely Translation Process Research (TPR). Different features were extracted from both the source and target parts of the TPR dataset, separately and different models were developed separately by employing such features using a machine learning framework. These models were trained using Support Vector Machine (SVM) and the best accuracy of 49.01% and 59.78% were obtained with respect to cross validation for source and target gaze fixation durations, respectively. The experiments were also carried out on the post edited data set using same experimental set up and the highest accuracy of 71.70% was obtained. Finally, Information Gain based pruning has been performed in order to select the best features that are useful for classifying the gaze durations.
机译：本文报道了为对眼动追踪数据集（即翻译过程研究（TPR））上的源和目标凝视持续时间进行分类而进行的各种实验。从TPR数据集的源部分和目标部分中提取了不同的特征，分别使用了机器学习框架并通过使用这些特征分别开发了不同的模型。这些模型是使用支持向量机（SVM）进行训练的，关于交叉注视源和目标注视持续时间的最佳准确性分别为49.01％和59.78％。还使用相同的实验设置在后编辑数据集上进行了实验，获得了71.70％的最高准确度。最后，已经执行了基于信息增益的修剪，以便选择可用于对凝视持续时间进行分类的最佳功能。

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