The rise in artificial intelligence and natural language processing techniques has increased considerably in the last few decades. 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 creation and analysis of poetry have been commonly carried out by hand, with a few computer-assisted approaches. In the Spanish context, the promise of machine learning is starting to pan out in specific tasks such as metrical annotation and syllabification. However, there is a task that remains unexplored and underdeveloped: stanza classification. 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. In this work, we analyzed different computational approaches to stanza classification in the Spanish poetic tradition. 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.

中文翻译：

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对于人工智能来说，一座桥梁太远了？：西班牙诗歌诗节的自动分类


过去几十年来，人工智能和自然语言处理技术的兴起大幅增长。从历史上看，焦点主要集中在以散文形式表达的文本上，而由于其丰富的语义和句法的复杂性而大多忽略了比喻或诗意的语言表达。诗歌的创作和分析通常是手工进行的，也有一些计算机辅助的方法。在西班牙语背景下，机器学习的前景开始在韵律注释和音节化等特定任务中得到体现。然而，有一项任务尚未探索和开发：节分类。对诗歌所依据的诗歌内部结构进行分类对于诗歌研究来说是一项特别相关的任务，因为它补充了诗歌的结构信息。在这项工作中，我们分析了西班牙诗歌传统中节分类的不同计算方法。这些方法表明，这项任务对于基于经典机器学习方法和统计语言模型的计算机系统来说仍然很困难，并且无法与基于专家知识的传统计算范式竞争。