论文标题
自动域适应性优于预测财务结果的手动域的适应
Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes
论文作者
论文摘要
在本文中，我们会自动创建情感词典来预测财务成果。我们比较了三种方法：（i）域总词典H4N的手动适应，（ii）H4N的自动适应和（iii）由第一手册，然后是自动适应的组合。在我们的实验中，我们证明了自动改编的情感词典优于先前的艺术状态，以预测财务结果的超额回报和波动性。特别是，自动适应性比手动适应更好。在我们的分析中，我们发现基于专家的先验信念的注释可能是不正确的 - 应根据目标域中的单词上下文来执行注释。
In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (I) manual adaptation of the domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first manual, then automatic adaptation. In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility. In particular, automatic adaptation performs better than manual adaptation. In our analysis, we find that annotation based on an expert's a priori belief about a word's meaning can be incorrect -- annotation should be performed based on the word's contexts in the target domain instead.
