Manshadi M., Swanson, R., Gordon, A.
Twenty-first International Conference of the Florida AI Society, Applied Natural Language Processing track
(Coconut Grove, FL, May 15-17, 2008)
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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.
