历史语言中的词语情感建模：种子词选择中的数量击败了假定的稳定性
Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection
摘要：
要理解历史文本，我们必须意识到语言--包括附加在词语上的情感内涵--会随着时间而变化。在本文中，我们的目的是估计的情感，这是与一个给定的词在以前的语言阶段的英语和德语。情绪是根据流行的效价-唤醒-支配（VAD）注释方案表示的。虽然更有表现力比极性单独，现有的词情感诱导方法通常不适合解决it. To克服这一限制，我们提出了两个流行的算法的改编VAD。为了衡量它们在历时环境中的有效性，我们提出了历史词汇情感的第一个黄金标准，这是由精通各自语言阶段的学者创建的，涵盖了英语和德语。与之前的研究结果相反，我们的研究结果表明，手工选择具有稳定情感意义的种子词实际上是有害的，而不是有益的。
英文摘要：
To understand historical texts, we must be aware that language -- including the emotional connotation attached to words -- changes over time. In this paper, we aim at estimating the emotion which is associated with a given word in former language stages of English and German. Emotion is represented following the popular Valence-Arousal-Dominance (VAD) annotation scheme. While being more expressive than polarity alone, existing word emotion induction methods are typically not suited for addressing it. To overcome this limitation, we present adaptations of two popular algorithms to VAD. To measure their effectiveness in diachronic settings, we present the first gold standard for historical word emotions, which was created by scholars with proficiency in the respective language stages and covers both English and German. In contrast to claims in previous work, our findings indicate that hand-selecting small sets of seed words with supposedly stable emotional meaning is actually harmful rather than helpful.
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。
