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We continue the study of the reproducibility of Propp’s annotations from Bod et al. (2012). We present four experiments in which test subjects were taught Propp’s annotation system; we conclude that Propp’s system needs a significant amount of training, but that with sufficient time investment, it can be reliably trained for simple tales.
We present a technique called event mapping that allows to project text representations into event lists, produce an event table, and derive quantitative conclusions to compare the text representations. The main application of the technique is the case where two classes of text representations have been collected in two different settings (e.g., as annotations in two different formal frameworks) and we can compare the two classes with respect to their systematic differences in the event table. We illustrate how the technique works by applying it to data collected in two experiments (one using annotations in Vladimir Propp’s framework, the other using natural language summaries).
A formal narrative representation is a procedure assigning a formal description to a natural language narrative. One of the goals of the computational models of narrative community is to understand this procedure better in order to automatize it. A formal framework fit for automatization should allow for objective and reproducible representations. In this paper, we present empirical work focussing on objectivity and reproducibility of the formal framework by Vladimir Propp (1928). The experiments consider Propp’s formalization of Russian fairy tales and formalizations done by test subjects in the same formal framework; the data show that some features of Propp’s system such as the assignment of the characters to the dramatis personae and some of the functions are not easy to reproduce.
We present an experimental approach to determining natural dimensions of story comparison. The results show that untrained test subjects generally do not privilege structural information. When asked to justify sameness ratings, they may refer to content, but when asked to state differences, they mostly refer to style, concrete events, details and motifs. We conclude that adequate formal models of narratives must represent such non-structural data.