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

Creación interactiva y colaborativa de historias

Esta memoria contiene la definición de una estructura capaz de guardar historias de una manera comprensible y coherente, que facilitará la investigación y el desarrollo de software. También contiene la implementación de una aplicación, persiguiendo dicho objetivo, TaleBox, que ha sido desarrollado como un juego de cartas por turnos y multijugador para dispositivos móviles, para la creación colaborativa e interactiva de historias. Ambas, estructura y aplicación, han sido desarrolladas con la ayuda de Ben Kybartas y GluNet, y bajo supervisión directa de Rafael Bidarra en la TU Delft, Delft, Países Bajos

Building a Bank of Semantically Encoded Narratives

We propose a methodology for a novel type of discourse annotation whose model is tuned to the analysis of a text as narrative. This is intended to be the basis of a “story bank ” resource that would facilitate the automatic analysis of narrative structure and content. The methodology calls for annotators to construct propositions that approximate a reference text, by selecting predicates and arguments from among controlled vocabularies drawn from resources such as WordNet and VerbNet. Annotators then integrate the propositions into a conceptual graph that maps out the entire discourse; the edges represent temporal, causal and other relationships at the level of story content. Because annotators must identify the recurring objects and themes that appear in the text, they also perform coreference resolution and word sense disambiguation as they encode propositions. We describe a collection experiment and a method for determining inter-annotator agreement when multiple annotators encode the same short story. Finally, we describe ongoing work toward extending the method to integrate the annotator’s interpretations of character agency (the goals, plans and beliefs that are relevant, yet not explictly stated in the text). 1