This paper describes submissions from the team Nostra Domina to the EvaLatin 2024 shared task of emotion polarity detection. Given the low-resource environment of Latin and the complexity of sentiment in rhetorical genres like poetry, we augmented the available data through automatic polarity annotation. We present two methods for doing so on the basis of the $k$-means algorithm, and we employ a variety of Latin large language models (LLMs) in a neural architecture to better capture the underlying contextual sentiment representations. Our best approach achieved the second highest macro-averaged Macro-$F_1$ score on the shared task's test set.

翻译：本文描述了Nostra Domina团队在EvaLatin 2024情感极性检测共享任务中的提交方案。针对拉丁语这一低资源语言环境以及诗歌等修辞体裁中情感的复杂性，我们通过自动极性标注扩充了可用数据。我们提出了两种基于$k$-means算法实现该目的的方法，并在神经架构中采用多种拉丁语大语言模型（LLMs），以更准确地捕获潜在的上下文情感表示。我们最佳方法在共享任务测试集上取得了排名第二的宏平均Macro-$F_1$分数。