Lines with multiple double morae of syllables with few characters are the most difficult. We determine that trochaic alternation with a one syllable anacrusis and words carrying clear stress assignment are the easiest for the model to scan. The model achieves a weighted average F-score of 0.925 on internal cross-validation and 0.909 on held-out testing data. Additional metrical rules are enforced and marginal probabilities are calculated to yield the most likely legal scansion of a line. The features used are: (1) the syllable's position within the line, (2) the syllable's length in characters, (3) the syllable's characters, (4) elision (last two characters of previous syllable and first two characters of focal syllable), (5) syllable weight, and (6) word boundaries. We construct a supervised Conditional Random Field (CRF) model to predict the metrical value of syllables, and subsequently investigate medieval German poets' use of semantic and sonorous emphasis through meter. The seventh value is a double mora, i.e., a long stressed syllable. Single or half mora syllables can carry any one of three types of stress, resulting in six combinations. There are a total of eight possible metrical values. The predominating pattern in MHG verse is the alternation between stressed and unstressed syllables, but syllable length also plays a crucial role. Middle High German (MHG) epic poetry presents a unique solution to the linguistic changes underpinning the transition from classical Latin poetry, based on syllable length, into later vernacular rhythmic poetry, based on phonological stress. Despite being initially conceived as models suitable for semantic tasks, our results suggest that transformers-based models retain enough structural information to perform reasonably well for Spanish on a monolingual setting, and outperforms both for English and German when using a model trained on the three languages, showing evidence of the benefits of cross-lingual transfer between the languages. In this paper, we compare the automated metrical pattern identification systems available for Spanish, English, and German, against fine-tuned monolingual and multilingual language models trained on the same task. This opens the door for interpretation and further complicates the creation of automated scansion algorithms useful for automatically analyzing corpora on a distant reading fashion. Some rhetorical devices shrink the metrical length, while others might extend it. Intricate language rules and their exceptions, as well as poetic licenses exerted by the authors, make calculating these patterns a nontrivial task. The splitting of words into stressed and unstressed syllables is the foundation for the scansion of poetry, a process that aims at determining the metrical pattern of a line of verse within a poem. These approaches show that this task continues to be hard for computers systems, both based on classical machine learning approaches as well as statistical language models and cannot compete with traditional computational paradigms based on the knowledge of experts. In this work, we analyzed different computational approaches to stanza classification in the Spanish poetic tradition. This classification of the inner structures of verses in which a poem is built upon is an especially relevant task for poetry studies since it complements the structural information of a poem. However, there is a task that remains unexplored and underdeveloped: stanza classification. In the Spanish context, the promise of machine learning is starting to pan out in specific tasks such as metrical annotation and syllabification. The creation and analysis of poetry have been commonly carried out by hand, with a few computerāassisted approaches. Historically, the focus has been primarily on texts expressed in prose form, leaving mostly aside figurative or poetic expressions of language due to their rich semantics and syntactic complexity. The rise in artificial intelligence and natural language processing techniques has increased considerably in the last few decades.
0 Comments
Leave a Reply. |