SGER: U3 - Understanding User Understanding

这个项目承担了必要的第一步，以了解医学文本中哪些简化和转换增加了理解。将比较来自WebMD、政府健康网站、患者教育材料和患者博客等网站的文档语料库的语法和词汇特征及其在复杂和简化文档样式中的出现频率。目标将是找到结构，新的或以前被其他人发现与理解有关的结构，这些结构出现在一组中而不出现在另一组中，或者频率明显较低。然后，该语料库将被用来开发第二个语料库和一个平行语料库，语料库中的句子包含复杂的语言结构，而平行语料库中包含简化版本。用户研究将有助于将理解与特定的结构和词汇联系起来。该项目将把重点放在老年人身上，因为他们在健康信息消费者中占很大比例，而且还在不断增长。如果成功，该项目将导致开发一种反映与理解困难有关的文本特征的度量标准，并开发一种“语内机器翻译”程序，从难懂的文本转向更容易理解的文本。智力的价值在于发现健康和医学文本中语言特征的系统性差异，这些差异是可以测量的，并与老年读者的理解有关。该项目特别适合作为SGER项目，因为有必要进行研究，以评估自动文本简化的帮助程度。这种医疗信息的自动简化必须是绝对准确的。导致不同含义的“简化”在医疗保健领域是不可接受的。同时，它必须是完全自动的，才能有效地简化已经在线并继续产生的大量文本。高质量、全自动的机器翻译目前还不能在不受限制的文本上实现，因此这一研究目标必须被归类为具有相当高的风险。然而，该领域对医学文本的限制以及对语言内“翻译”的应用使这一目标变得更加可信。数百万人在线阅读健康信息，但许多人缺乏对这些信息的理解。这种对健康信息的误解增加了不明智决定的数量，并导致更糟糕的健康状况和更高的医疗成本。即使是读者理解力的微小改善也会产生重大影响，因为这可能会导致更少的不明智决定。更广泛的影响在于自动简化医学文本的计算方法，以及即使是理解上的微小增长可能对医疗保健产生的影响。这项研究如果成功，将为适合医学文本自动简化的文本结构指明方向。这有可能使消费者更容易获得大量基于网络的医疗和健康信息，从而产生更多知情的患者，并最终获得更好的结果。这项研究还可能为医疗保健提供者和患者之间新出现的电子通信现象提供一些指导。

This project undertakes the first steps necessary to learn which simplifications and transformations in medical text increase understanding. A corpus of documents from sites such as WebMD, government health sites, patient educational material and patient blogs will be compared for grammatical and vocabulary features and their frequency of occurrence in both complex and simplified document styles. The goal will be to find structures, - new or previously found by others to be associated with understanding, - that appear in one set but not in the other, or with significantly lower frequency. This corpus will then be used to develop a second corpus with sentences containing the difficult linguistic structures and a parallel corpus with simplified versions. A user study will help relate understanding to specific structures and vocabulary. The project will focus on seniors because they constitute a large and growing portion of health information consumers. If successful, this project will lead to the development of a metric that reflects text characteristics associated with comprehension difficulties and the development of an "intra-lingual machine translation" program to move from difficult to easier-to-understand text. The intellectual merit lies in discovery of systematic differences in linguistic features in health and medical text that can be measured and that are associated with understanding by senior readers. The project is especially suitable as a SGER project because studies are necessary to evaluate the degree to which automatic text simplification can help. Such automatic simplification of medical information must be absolutely accurate. "Simplifications" that result in a different meaning are not acceptable within the healthcare field. At the same time, it must be fully automatic if it is be useful in simplifying the vast amounts of text already on line and that continues to be produced. High quality, fully automatic machine translation is currently not achievable on unrestricted text, so this research goal must be classified as of fairly high risk. However, limitation of the domain to medical texts and the application to within-language "translation" make this goal more plausible.Millions of people read health information online but many lack understanding of this information. Such misunderstanding of health information increases the number of unwise decisions and leads to poorer health and higher healthcare costs. Even a small improvement in readers' understanding will have a significant impact because it may lead to fewer unwise decisions. The broader impact lies in computational approaches to automatic simplification of medical texts and the impact that even a small increase in understanding may have on healthcare. This research, if successful, will point the way towards structures in text suitable for automatic simplification of medical texts. This has the potential to make the vast amount of web-based medical and health information more accessible to consumers, resulting in more informed patients, and ultimately better outcomes. The research may also provide some guidelines for the newly emerging phenomenon of electronic communication between healthcare providers and patients.