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首页> 美国政府科技报告 >Automatically Generating Reading Comprehension Look-Back Strategy: Questions from Expository Texts
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Automatically Generating Reading Comprehension Look-Back Strategy: Questions from Expository Texts

机译：自动生成阅读理解回顾策略：来自说明文本的问题

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摘要

Learning to read is an important skill for both children and adults, whether it takes place in their first language or their second language. According to the National Center for Educational Statistics 2003 Assessment (2007), 5% of adults (16 years and older) in the United States are functionally non-literate. Adults who enroll in literacy programs in the United States desire to improve reading skills for a variety of reasons. But resources are limited, as literacy programs often rely on volunteer tutors and variable funding sources. Computers are increasingly used to assist in the tutoring process. Improving the computer's ability to aid in this process with useful tools for instructors and students will allow students access to more reading materials and provide them with more opportunities to read. The primary goal for everyone is to increase reading and reading ability. For instructors, writing questions for reading comprehension exercises is time-consuming and difficult. The questions are only useful for a single text. Authoring tools such as Conduit's Dasher (1993), Half-baked Software's Hotpotatoes (2003-2007), and the University of Arizona's MaxAuthor (2007) make the task somewhat easier with tools for creating exercises, and more productive by means of allowing instructors to share exercises on the Internet. Reading comprehension is composed of several skill levels that work with various language or text chunks: awareness of phonemes, decoding skills, fluency vocabulary knowledge, grammar skills, and meta-learning skills. The goal of the project described in this report was to create a system that could automatically and accurately generate fact-based reading comprehension questions with expository text for teaching or using a specific look-back reading strategy. The project also aimed to use existing Natural Language programs and knowledge sources as well as implementing new ways to combine their output.

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