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

EAGER：预测领域级阅读理解难度以支持成人学习

基本信息

批准号：
1748771
负责人：
Ani Nenkova
金额：
$ 17.22万
依托单位：
University of Pennsylvania
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1748771&HistoricalAwards=false
关键词：
EAGER Predicting Domain level Reading

项目摘要

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.

无论他们的教育水平如何，现代劳动力的参与者都将灵活地在新的，通常是技术领域的新技术领域阅读和学习的能力上，包括科学子场，医学，政策和护理。但是，值得注意的是，对于此类学习的技术支持几乎没有（如果有的话）通常是动态的，并且需要根据需要阅读文本，而不是像课堂学习一样在有序的课程中阅读文本。这项探索性研究的早期赠款通过探索模拟专家读者在识别重要内容和绘制推断以连接丰富背景知识和手头的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的需求。将为读者提供模拟专家的推论和重要性判断的试点实施，以集中注意力并加强对文本的理解。专家读者行为的探索模型的这种发展依赖于将文本词汇表征为技术和普通语言的技术，通过将单词的出现统计数据与域中的统计数据进行了对比，例如在随机的电话对话或新闻中随机示例中。它还将探讨定义挖掘技术的适应，并从大量典型域文本中得出原型事件序列和浅本体。