论文标题
一个尺寸不适合全部：个性化单词复杂性模型的情况
One Size Does Not Fit All: The Case for Personalised Word Complexity Models
论文作者
论文摘要
复杂的单词识别（CWI）旨在检测读者可能难以理解的文本中的单词。已经表明，CWI系统可以改善文本简化，可读性预测和词汇获取建模。但是，单词的困难是一个高度特殊的概念，取决于读者的母语，熟练程度和阅读经验。在本文中，我们表明，在为单个读者预测单词复杂性时，个人模型是最好的。我们使用一个新颖的主动学习框架，该框架可以为个人量身定制模型，并释放复杂性注释和模型的数据集作为进一步研究的基准。
Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acquisition modelling. However, the difficulty of a word is a highly idiosyncratic notion that depends on a reader's first language, proficiency and reading experience. In this paper, we show that personal models are best when predicting word complexity for individual readers. We use a novel active learning framework that allows models to be tailored to individuals and release a dataset of complexity annotations and models as a benchmark for further research.
