Modeling common sense knowledge via scripts
It is generally believed that incorporation of common sense knowledge about the world would benefit natural language understanding systems. This dissertation considers modeling common sense knowledge associated with everyday activities. The common sense knowledge about everyday activities can be understood and modeled using the concept of scripts. Scripts are sequences of events describing stereotypical human activities (i.e. scenarios) for example, cooking pasta, baking a cake, etc. Scripts have been shown to be useful for drawing inferences from text. Consequently, scripts find applications in the area of natural language processing (NLP) and artificial intelligence (A.I. for example, temporal reasoning, coreference resolution, story understanding and question answering systems. To model script knowledge, three main tasks need to be addressed: event ordering, event paraphrasing and event prediction. This dissertation introduces a novel probabilistic neural network based model for learning event ordering and event paraphrasing from a crowdsourced scripts corpus and the Gigaword news corpus. The model outperforms currently prevalent graph based and count based methods. In addition to event ordering and paraphrasing, a script understanding system should have the ability to predict an upcoming event in a script. We propose an extension of the event ordering model for event prediction. Experiments on a movie summary corpus show the advantages of the event prediction model over existing count based methods.