{"id":{"repo_id":"lsu-thes","oai_identifier":"oai:repository.lsu.edu:gradschool_dissertations-1894"},"canonical_url":"https://search.dev.ndltd.org/etd/lsu-thes/oai:repository.lsu.edu:gradschool_dissertations-1894","repository":{"repo_id":"lsu-thes","name":"Lousiana State University","base_url":"https://repository.lsu.edu/do/oai/"},"display":{"title":"Toward digitizing the human experience : a new resource for natural language processing","abstract":"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.","abstract_html":"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. 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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."]},{"key":"dc:title","label":"Title","values":["Toward digitizing the human experience : a new resource for natural language processing"]}]}],"canonical_facts":{"dc:creator":["Weltman, Jerry Scott"],"dc:date":["2013-03-27"],"dc:date.available":["2022-05-12T23:10:21Z"],"dc:description.abstract":["A long-standing goal of Artificial Intelligence is to program computers that understand natural language. 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