Natural language processing has made progress in incorporating human context
into its models, but whether it is more effective to use group-wise attributes
(e.g., over-45-year-olds) or model individuals remains open. Group attributes
are technically easier but coarse: not all 45-year-olds write the same way. In
contrast, modeling individuals captures the complexity of each person's
identity. It allows for a more personalized representation, but we may have to
model an infinite number of users and require data that may be impossible to
get. We compare modeling human context via group attributes, individual users,
and combined approaches. Combining group and individual features significantly
benefits user-level regression tasks like age estimation or personality
assessment from a user's documents. Modeling individual users significantly
improves the performance of single document-level classification tasks like
stance and topic detection. We also find that individual-user modeling does
well even without user's historical data.

自然语言处理在整合人类背景的模型方面取得了进展，但使用群体属性（例如超过 45 岁的人）还是个体建模更有效尚未确定。比较群体属性、个体用户和结合方法对建模人类背景的影响，发现群体和个体特征的组合显著改善了用户级回归任务，而个体用户建模显著提高了单篇文档级分类任务的性能。此外，即使没有用户的历史数据，个体用户建模也表现出良好的效果。