  Motifs are distinctive recurring elements found in folklore that have
significance as communicative devices in news, literature, press releases, and
propaganda. Motifs concisely imply a large constellation of culturally-relevant
information, and their broad usage suggests their cognitive importance as
touchstones of cultural knowledge, making their detection a worthy step toward
culturally-aware natural language processing tasks. Until now, folklorists and
others interested in motifs have only extracted motifs from narratives
manually. We present a preliminary report on the development of a system for
automatically detecting motifs. We briefly describe an annotation effort to
produce data for training motif detection, which is on-going. We describe our
in-progress architecture in detail, which aims to capture, in part, how people
determine whether or not a motif candidate is being used in a motific way. This
description includes a test of an off-the-shelf metaphor detector as a feature
for motif detection, which achieves a F1 of 0.35 on motifs and a macro-average
F1 of 0.21 across four categories which we assign to motif candidates.
