Question Answering in Natural Language Narratives Using Symbolic Probabilistic Reasoning

We present a framework to represent and reason about nar- ratives. We build a symbolic probabilistic representation of the temporal sequence of world states and events implied by a narrative using statistical approaches. We show that the combination of this representation together with domain knowledge and symbolic probabilistic reasoning algorithms enables understanding of a narrative and answering semantic questions whose responses are not contained in the narrative. In our experiments, we show the power of our framework (vs. traditional approaches) in answering semantic questions for two domains of RoboCup soccer commentaries and early reader children stories focused on spatial contexts.