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Description:
Originating in mythological research, the Hylistic theory introduces hylemes as a basic plot unit, containing actions, states, or background information derived from a source. These statements are organized in hyleme sequences which are derived by domain experts and concern a narrative variant. By using those sequences, narrative variants can be compared across different source materials, such as text genres or sources in different languages, and subsequently structurally aligned with variants of the same narrative. Establishing appropriate methods for the automation of these hyleme sequene alignments is the central objective of this work. This thesis presents the first approach towards a Digital Hylistic theory. This work is related to the research discipline of Computational Narratology, and its related areas. Two data sets are the basis of the conceptual and exploratory studies undertaken in this work: the German hyleme data sets consists of sequences from different cultural and temporal backgrounds, including Ancient Greece, or Mesopotamia. Those sequences are extracted by domain experts in the fields of Classics, Ancient Near Eastern Studies, and Religious Studies as part of the DFG-funded myth research group 2064 STRATA. They are derived from a multitude of sources, in different source languages, and from different genres and styles of narratives. The second data set is the first ever English hyleme data set, containing sequences describing a set of Zulu folktales. The data set is also the first approach towards a hylistic representation of folkloristic (in contrast to mythological) material. This thesis follows a multi-method approach, grounded in the narrative theory of Hylistics, that carefully models hylemes and their properties as objects that can be processed and analyzed using methods from the fields of Natural Language Processing, Knowledge Engineering, and Formal Languages. This work approaches the comparability of hylemes from a semantic similarity point-of-view. The problem of aligning hyleme ...