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This paper investigates the integration of eye-tracking data with NLP techniques to understand cognitive processes during reading and improve NLP model performance. Using the Tsukuba Eye-tracking Corpus (TECO), the authors applied random forest and linear regression models to analyze eye movement patterns and correlate them with reading performance. The results demonstrate that eye-tracking data provides valuable indicators of text complexity and reader comprehension, leading to more efficient and accurate NLP models.
Eye-tracking data can be effectively integrated with NLP models to improve reading comprehension analysis and enhance tasks like machine translation and summarization.
Introduction: This research investigates the integration of natural language processing (NLP) techniques with eye-tracking data to gain deeper insights into cognitive processes during reading. By analyzing eye movements, such as saccades and fixations, the study aims to enhance NLP models' accuracy and efficiency in processing text complexity and comprehension. Objectives: This study objective is to analyze eye-tracking data to understand cognitive processes in reading, identify patterns linked to reading difficulties, and enhance NLP models for tasks like semantic analysis and readability evaluation. Additionally, it addresses methodological and technical challenges in integrating eye-tracking data into NLP systems. Methods: The research utilizes the Tsukuba Eye-tracking Corpus (TECO), which contains data from Japanese students learning English. Two machine learning models—random forest and linear regression—are applied to analyze eye movement patterns. Feature engineering techniques, including data cleaning, feature selection, and outlier handling, are employed to extract syntactic and semantic complexities. Results: Findings reveal that eye-tracking data provides valuable indicators of text complexity and reader comprehension. The machine learning models effectively correlate eye movement patterns with reading performance, demonstrating the potential of integrating cognitive signals into NLP applications. Conclusions The study highlights the benefits of using eye-tracking data to enhance NLP tasks, including machine translation, text completion, and summarization. The results suggest that incorporating cognitive signals into NLP systems can lead to more efficient and accurate models, offering advancements in human-computer interaction and artificial intelligence applications.
