Multi Modal Distance - An Approach to Stemma Generation With Weighting

Abstract

Stemma generation can be understood as a task where an original manuscript M gets copied and copies – due to the manual mode of copying – vary from each other and from M . Copies $M_1 , .. , M_k$ which survive historical loss serve as input to a mapping process estimating a directed acyclic graph (tree) which is the most likely representation of their copy history. One can first tokenize and align the texts of $M_1, .., M_k$ and then produce a pairwise distance matrix between them. From this, one can finally derive a tree with various methods, for instance Neighbor-Joining (NJ) (Saitou and Nei, 1987). For computing those matrices, previous research has applied unweighted approaches to token similarity (implicitly interpreting each token pair as a binary observation: identical or different), see Mooney et al. (2003). The effects of weighting have then been investigated and Spencer et al. (2004b) found them to be small in their (not necessarily all) scenario(s). The present approach goes beyond the token level and instead of a binary comparison uses a distance model on the basis of psycholinguistically gained distance matrices of letters in three modalities: vision, audition and motorics. Results indicate that this type of weighting have positive effects on stemma generation.

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