Human Experience Mining for Context-aware Computing

Abstract

The Web can be seen as a massive memory for human activities and experiences. Not only do online news papers report on various events the public would be interested in but also personal and social media such as blog posts and twits reflect various personal activities and experiences that could be gathered and utilized for various decision making purposes. We have embarked on projects whose common goal is to utilize the vast amount of information on human experiences involving activities. In this talk, I am going to introduce a method for mining personal experiences from a large set of weblogs. We define experience as knowledge embedded in a collection of activities or events which an individual or group has actually undergone. Based on an observation that experience-revealing sentences have a certain linguistic style, we formulate the problem of detecting experience as a classification task using various features including tense, voice, mood, aspect, modality, experiencer, and verb classes. We believe experiential knowledge constructed with a further analysis of experience-revealing sentences can allow for query-free search and “decision-on-the-go” that are essential in a mobile/smart phone environment.