Episodic knowledge is often stored in the form of textual narratives written in natural language. However, a large repository of such narratives will contain both repetitive and novel knowledge. In this paper, we propose an approach for discovering interesting pieces of knowledge by using a priori task knowledge. By considering the narratives as generated by an underlying task structure, the elements of the task can be regarded as topics that generate the text. Then, by capturing task content in a probabilistic model, the model can be used, e.g., to identify the semantic orientation of textual phrases. An evaluation for a real world corpus of episodic narratives provides strong evidence for the feasibility of the proposed approach.