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.

翻译：自然语言处理在将人类语境融入模型方面取得了进展，但究竟使用群体属性（例如45岁以上人群）更有效，还是对个体进行建模更为有效，这一问题仍待探讨。群体属性在技术层面更易实现，但较为粗糙：并非所有45岁人群的写作风格都相同。相比之下，对个体建模能够捕捉每个人身份的复杂性，从而提供更个性化的表征，但可能需要处理无限数量的用户，并依赖可能难以获取的数据。我们比较了通过群体属性、个体用户以及两者结合的方法来建模人类语境的效果。将群体与个体特征相结合，能显著提升用户层面的回归任务（如根据用户文档估计年龄或评估个性）的性能。对个体用户进行建模则能显著改善单文档层面的分类任务（如立场检测和主题识别）的表现。我们还发现，即使没有用户的历史数据，个体用户建模也能取得良好效果。