Learning a Probabilistic Model of Event Sequences from Internet Weblog Stories

One of the central problems in building broad-coverage story understanding systems is generating expectations about event sequences, i.e. predicting what happens next given some arbitrary narrative context. In this paper, we describe how a large corpus of stories extracted from Internet weblogs was used to learn a probabilistic model of event sequences using statistical language modeling techniques. Our approach was to encode weblog stories as sequences of events, one per sentence in the story, where each event was represented as a pair of descriptive key words extracted from the sentence. We then applied statistical language modeling techniques to each of the event sequences in the corpus. We evaluated the utility of the resulting model for the tasks of narrative event ordering and event prediction. Story Understanding Systems Automated story understanding has proved to be an extremely difficult task in natural language processing. Despite a rich history of research in automatic narr...

• Type: Conference
• Year: 2008
• Where: FLAIRS
• Authors: Mehdi Manshadi, Reid Swanson, Andrew S. Gordon
• Keywords: Artificial Intelligence, Event Sequences, FLAIRS 2008, Story Understanding, Story Understanding Systems