Automatic unknown word detection techniques can enable new applications for assisting English as a Second Language (ESL) learners, thus improving their reading experiences. However, most modern unknown word detection methods require dedicated eye-tracking devices with high precision that are not easily accessible to end-users. In this work, we propose GazeReader, an unknown word detection method only using a webcam. GazeReader tracks the learner's gaze and then applies a transformer-based machine learning model that encodes the text information to locate the unknown word. We applied knowledge enhancement including term frequency, part of speech, and named entity recognition to improve the performance. The user study indicates that the accuracy and F1-score of our method were 98.09% and 75.73%, respectively. Lastly, we explored the design scope for ESL reading and discussed the findings.

本文提出了一种使用网络摄像头进行未知单词检测的方法，通过跟踪学习者的凝视并应用基于转换器的机器学习模型，以识别文本中的未知单词，并进行使用频率、词性和命名实体识别等知识增强措施以提高性能，用户研究表明其方法的准确率和F1分数分别为98.09%和75.73%，最后，本研究探讨了ESL阅读设计的范围和发现。

使用网络摄像头检测英语作为第二语言（ESL）学习者的不认识的单词的GazeReader