In this work, we present Web-STAR, an online platform for story understanding built on top of the STAR (STory comprehension through ARgumentation) reasoning engine. This platform includes a web-based IDE, integration with the STAR system and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of "social" features like public story sharing with a built-in commenting system, a public repository for sharing stories with the community and collaboration tools that can be used from both project team members for development and educators for teaching. Moreover, we discuss the ongoing work on adding new features and functionality to this platform.
Massachusetts Institute of Technology. Dept. of Electrical Engineering. Thesis. 1972. Ph.D. This paper describes Ms. Malaprop, a program (currently being designed) which will answer questions about simple stories dealing with painting, where stories, questions and answers will be expressed in semantic representation rather than English in order to allow concentration on the inferential problems involved in language comprehension. The common sense knowledge needed to accomplish the task is provided by the frame representation of "mundane" painting found in Charniak (1976b). The present paper, after reviewing this representation, goes on to describe how it is used by Ms. Malaprop. Some specific questions of matching, correcting false conclusions, and search, will be discussed. This study investigated the automatic modelling of space and time in narratives involving dining in a restaurant. We built a program that (1) uses information extraction techniques to convert narrative texts into templates containing key information about the dining episodes discussed in the narratives, (2) constructs commonsense reasoning problems from the templates, (3) uses commonsense reasoning and a commonsense knowledge base to build models of the dining episodes, and (4) generates and answers questions by consulting the models. We describe the program and present the results of running it on a corpus of web texts and American literature. This paper describes initial work on Deep Read, an automated reading comprehension system that accepts arbitrary text input (a story) and answers questions about it. We have acquired a corpus of 60 development and 60 test stories of 3rd to 6th grade material; each story is followed by short-answer questions (an answer key was also provided). We used these to construct and evaluate a baseline system that uses pattern matching (bag-of-words) techniques augmented with additional automated linguistic processing (stemming, name identification, semantic class identification, and pronoun resolution). This simple system retrieves the sentence containing the answer 30--40 of the time. A story summarizer benefits greatly from a reader model because a reader model enables the story summarizer to focus on delivering useful knowledge in minimal time with minimal eort. Such a