论文标题

神圣喜剧的音节

Syllabification of the Divine Comedy

论文作者

Asperti, Andrea, Bianco, Stefano Dal

论文摘要

我们使用概率和约束编程的技术为神圣喜剧提供了音节缩写算法。我们特别关注的是Synalephe,它是根据一个单词的“倾向”来解决的,以与Synalephe一起用相邻的单词参加。我们共同提供一个在线词汇量,其中包含每个单词,有关其音节的信息,滋补口音的位置以及左侧和右侧的上述Synalephe倾向。该算法本质上是非确定性的,为每个经文产生不同的可能的音节,具有不同的可能性。在第10,第4和第6个音节中相对于重音的度量限制用于进一步降低解决方案空间。因此,最有可能的音节缩放是作为输出返回。我们认为,这项工作可能是许多不同调查的主要里程碑。从数字人文科学的角度来看,它为计算机辅助分析数字来源的新观点开辟了新的观点,包括对异常情况和有问题的案例的自动检测,经文及其分类的公制聚类,或者是针对例如。辅音和元音的语音作用。从文本处理和深度学习的角度来看,有关音节的信息和口音位置的信息可以开辟各种令人兴奋的观点,从自动学习单词和经文的可能性,到改善生成模型,了解度量问题以及对预期音乐性的更加尊重。

We provide a syllabification algorithm for the Divine Comedy using techniques from probabilistic and constraint programming. We particularly focus on the synalephe, addressed in terms of the "propensity" of a word to take part in a synalephe with adjacent words. We jointly provide an online vocabulary containing, for each word, information about its syllabification, the location of the tonic accent, and the aforementioned synalephe propensity, on the left and right sides. The algorithm is intrinsically nondeterministic, producing different possible syllabifications for each verse, with different likelihoods; metric constraints relative to accents on the 10th, 4th and 6th syllables are used to further reduce the solution space. The most likely syllabification is hence returned as output. We believe that this work could be a major milestone for a lot of different investigations. From the point of view of digital humanities it opens new perspectives on computer assisted analysis of digital sources, comprising automated detection of anomalous and problematic cases, metric clustering of verses and their categorization, or more foundational investigations addressing e.g. the phonetic roles of consonants and vowels. From the point of view of text processing and deep learning, information about syllabification and the location of accents opens a wide range of exciting perspectives, from the possibility of automatic learning syllabification of words and verses, to the improvement of generative models, aware of metric issues, and more respectful of the expected musicality.

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