论文本数据在生活日志中的地位及基于语义层面的文本分析
On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets
摘要：
目前生活日志数据的研究还没有足够的重视分析的认知活动相比，身体活动。我们认为，展望未来，可穿戴设备将变得更便宜、更普遍，文本数据将发挥更重要的作用。生活记录设备捕获的数据将越来越多地包括语音和文本，这可能有助于分析智力活动。通过分析一个人听到、读到和看到的内容，我们应该能够衡量一个学习者对某个主题或科目的认知活动的程度。基于测试的生活日志记录可以受益于为自然语言处理开发的语义分析工具。我们展示了如何通过使用分类主题方面，这些方面可能是有用的量化认知活动，致力于在一个人的一天中的各种主题，可以实现这样的文本数据的语义分析。我们目前正在开发一种方法来自动创建分类主题词汇表，可以应用于这种检测的智力活动。
英文摘要：
Current research in lifelog data has not paid enough attention to analysis of cognitive activities in comparison to physical activities. We argue that as we look into the future, wearable devices are going to be cheaper and more prevalent and textual data will play a more significant role. Data captured by lifelogging devices will increasingly include speech and text, potentially useful in analysis of intellectual activities. Analyzing what a person hears, reads, and sees, we should be able to measure the extent of cognitive activity devoted to a certain topic or subject by a learner. Test-based lifelog records can benefit from semantic analysis tools developed for natural language processing. We show how semantic analysis of such text data can be achieved through the use of taxonomic subject facets and how these facets might be useful in quantifying cognitive activity devoted to various topics in a person's day. We are currently developing a method to automatically create taxonomic topic vocabularies that can be applied to this detection of intellectual activity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
