Abstract:

Quantitative metrics, and particularly the statistical study of meter and rhyme, has been a core research methodology in Russian verse theory and scholarship at least since the early twentieth century both among Russian scholars (e.g., Belyj, Taranovski, Gasparov) and abroad (e.g., Shaw, Scherr, Friedberg). The goals of this research converge around the identification of patterns, tendencies, and trends in poets, schools, and periods on the basis of poetic corpora, as well as the exploration of the semantic haloes of formal features, that is, of the way in which formal structures become associate with and therefore come to convey meaning. The principal limitation to using available plain-text files for machine-assisted analysis of this type has been that the place of stress in words, which in Russian is not predictable without linguistic knowledge and which is not part of standard orthographic practice, must be known before the orthographic representation can be converted algorithmically to a useful phonetic representation, which is, in turn, a prerequisite for identifying meter and rhyme automatically. This presentation describes the design and development of a system that addresses these research prerequisites by performing metrical and rhyme analysis on the basis of unstressed plain-text natural-language-orthography input.