.png){kind=icon}
返回
Assessing user-specific difficulty of documents
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ipm.2012.04.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
On the web, a huge variety of text collections contain knowledge in different expertise domains, such as technology or medicine. The texts are written for different uses and thus for people having different levels of expertise on the domain. Texts intended for professionals may not be understandable at all by a lay person, and texts for lay people may not contain all the detailed information needed by a professional. Many information retrieval applications, such as search engines, would offer better user experience if they were able to select the text sources that best fit the expertise level of the user. In this article, we propose a novel approach for assessing the difficulty level of a document: our method assesses difficulty for each user separately. The method enables, for instance, offering information in a personalised manner based on the user's knowledge of different domains. The method is based on the comparison of terms appearing in a document and terms known by the user. We present two ways to collect information about the terminology the user knows: by directly asking the users the difficulty of terms or, as a novel automatic approach, indirectly by analysing texts written by the users. We examine the applicability of the methodology with text documents in the medical domain. The results show that the method is able to distinguish between documents written for lay people and documents written for experts. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Difficulty measure
User modelling
Readability
Keyphrase extraction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
6.9
论文数:
5.2K
被引数:
1.4W
机构
引用论文
.png){kind=icon}
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions走向下一代推荐系统: 对最新技术和可能扩展的调查
PREAI
分享收藏
查看更多内容