Toward digitizing the human experience : a new resource for natural language processing
A long-standing goal of Artificial Intelligence is to program computers that understand natural language. A basic obstacle is that computers lack the common sense that even small children acquire simply by experiencing life, and no one has devised a way to program this experience into a computer. This dissertation presents a methodology and proof-of-concept software system that enables non-experts, with some training, to create simple experiences. For the purposes of this dissertation, an experience is a series of time-ordered comic frames, annotated with the changing intentional and physical states of the characters and objects in each frame. Each frame represents a small action and the effects of that action. To create an annotated experience, the software interface guides non-experts in identifying facts about experiences that humans normally take for granted. As part of this process, it uses the Socratic Method to help users notice difficult-to-articulate commonsense data. The resulting data is in two forms: specific narrative statements and general commonsense rules. Other researchers have proposed similar narrative data for commonsense modeling, but this project opens up the possibility of non-experts creating these data types. A test on ten subjects suggests that non-experts are able to use this methodology to produce high quality experiential data. The system’s inference capability, using forward chaining, demonstrates that the collected data is suitable for automated processing.
natural language processing, common sense, knowledge acquisition
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