西班牙诗歌的词汇语义和情感建模：一种半监督学习方法
Lexico-semantic and affective modelling of Spanish poetry: A semi-supervised learning approach
摘要：
在过去的几年中，通过使用Transformers，文本分类任务有了很大的改进。然而，大多数研究集中在散文文本，诗歌很少受到关注，特别是西班牙语。在本文中，我们提出了一个半监督学习的方法来推断21个心理类别引起的语料库的4572十四行诗，以及10个情感和词汇语义多类。用于训练评估的诗歌子集包括270首十四行诗。通过我们的方法，我们实现了76%的心理类别的AUC超过0.7，60%的多类别的AUC超过0.65。十四行诗的建模使用Transformers，通过句子嵌入，随着词汇语义和情感特征，通过使用外部词汇。因此，我们看到这种方法提供了高达0.12的AUC增加，而不是单独使用Transformers。
英文摘要：
Text classification tasks have improved substantially during the last years by the usage of transformers. However, the majority of researches focus on prose texts, with poetry receiving less attention, specially for Spanish language. In this paper, we propose a semi-supervised learning approach for inferring 21 psychological categories evoked by a corpus of 4572 sonnets, along with 10 affective and lexico-semantic multiclass ones. The subset of poems used for training an evaluation includes 270 sonnets. With our approach, we achieve an AUC beyond 0.7 for 76% of the psychological categories, and an AUC over 0.65 for 60% on the multiclass ones. The sonnets are modelled using transformers, through sentence embeddings, along with lexico-semantic and affective features, obtained by using external lexicons. Consequently, we see that this approach provides an AUC increase of up to 0.12, as opposed to using transformers alone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
