Abstract
The automatic metric analysis (commonly referred to as scansion ) of Spanish poetry is not a trivial problem since it combines the nuances of the language, the different poetic traditions related to melodic patterns, and the personal stylistic preferences and intentions of the author. In this paper, we explore two alternative algorithmic approaches tailored to different applications scenarios. The first approach, Rantanplan, is a rule-based method that consists of four Natural Language Processing modules that work together to perform scansion and other related analysis: Part of Speech tagging, syllabification, stress assignment, and metrical adjustment. The second approach, Jumper, explores the possibility of performing scansion without syllabification, with a twofold purpose: to minimize the errors propagated in different parts of the linguistic processing pipeline (including the syllabification step), and to improve the efficiency of the process. Both systems outperform the state of the art and provide either a more informative solution (suitable, for instance, for teaching purposes) or a more efficient processing (when a correct scansion is all the linguistic knowledge required, as in scholar philological studies). The combined use of both systems turns out to provide a practical tool to clean-up manual annotation errors in corpora.
Highlights
In recent years, several systems for automating parts of the literary analysis have emerged, enabling corpus linguistic approaches on poetry corpora that would otherwise need unmanageable amounts of expensive manual annotation
Rantanplan is a scansion tool conceived to assist researchers on the metrical analysis of a work and to be used as a source of information for populating a new ontology devoted to poetic works proposed by the POSTDATA ERC project [46]
This ontology is a three-layer encapsulated ontology aligned with FRBRoo foundational ontology [47] that models the primary information of a poem from the abstract concept of poetic work (i.e. FRBRoo work class), through the textual information of poem where all the poem metrical information is included with a fine degree of granularity [48]
Summary
Several systems for automating parts of the literary analysis have emerged, enabling corpus linguistic approaches on poetry corpora that would otherwise need unmanageable amounts of expensive manual annotation. G. Marco et al.: Automated Metric Analysis of Spanish Poetry: Two Complementary Approaches approach, Rantanplan (evolved from [2]), is a rule-based method that consists of four Natural Language Processing (NLP) modules that work together to perform scansion and other related analysis: PoS tagger, syllabification, stress assignment, and metrical adjustment. Its output is well-suited for researchers, poets or translators, and it is related to metre: metrical pattern, position of rhetorical devices, verse type, and self-assessed confidence in the classification of the pattern. Overall, both systems substantially improve previous state-of-the-art approaches (including rule-based and neural network-based methods), and provide optimal alternatives depending on the application scenario.
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