EAGER: Predicting Domain-level Reading Comprehension Difficulty to Support Adult Learning

无论他们的教育水平如何，现代劳动力的参与者都被期望能够灵活地阅读和学习新的，通常是技术领域的能力，包括科学子领域，医学，政策和护理。然而，值得注意的是，这种学习几乎没有（如果有的话）技术支持，这种学习往往是动态的，需要根据需要阅读文本，而不是像课堂学习那样在有序的课程中学习。这项探索性研究的早期资助通过探索模拟专家读者在识别重要内容和绘制推理以连接丰富的背景知识和手边文本方面的行为的可行性，解决了技术领域成人学习的技术支持需求。读者将获得模拟专家的推理和重要性判断的试点实施，以集中他们的注意力并加强他们对文本的理解。专家读者行为的探索性模型的发展依赖于通过对比领域和一般文本（如电话交谈或新闻的随机样本）中的单词出现统计数据，将文本词汇表征为技术语言和普通语言的技术。它还将探讨定义挖掘和从大量典型领域文本中派生原型事件序列和浅层本体的技术的适应性。

Regardless of their level of education, participants in the modern workforce are expected to be flexible in their ability to read and learn in new, often technical domains, including scientific subfields, medicine, policy, and care. Remarkably, however, there is little, if any, technological support for such learning, which often is dynamic and requires reading texts as needed rather than in an ordered curriculum as in classroom learning. This Early Grant for Exploratory Research addresses the need for technological support for adult learning in technical domains by exploring the feasibility of simulating the behavior of an expert reader in identifying important content and drawing inferences to connect rich background knowledge and the text at hand. Readers will be provided access to pilot implementations of the inferences and importance judgements of the simulated expert, to focus their attention and strengthen their comprehension of the text. This development of exploratory models of expert reader behavior relies on techniques for characterizing text vocabulary into technical and plain language by contrasting word occurrence statistics in the domain and in general text such as in a random sample of telephone conversations or news. It will also explore the adaptation of techniques for definition mining and for deriving prototypical event sequences and shallow ontologies from large volume of typical domain text.