This paper describes work on automatically identifying categories of narrative clauses in personal stories written by ordinary people about their daily lives and experiences. We base our approach on Labov & Walet-zky's theory of oral narrative which categorizes narrative clauses into subtypes, such as ORIENTATION, ACTION and EVALUATION. We describe an experiment where we annotate 50 personal narratives from weblogs and experiment with methods for achieving higher annotation reliability. We use the resulting annotated corpus to train a classifier to automatically identify narrative categories, achieving a best average F-score of .658, which rises to an F-score of .767 on the cases with the highest annotator agreement. We believe the identified narrative structure will enable new types of computational analysis of narrative discourse.
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