自动识别实体提及的未来语境，助力决策与趋势预测。
Detecting Future-related Contexts of Entity Mentions
- 构建包含19,540句的实体中心文本数据集，标注未来与非未来语境。
- 在无明确时间词情况下，评估多种语言模型对未来内容的识别能力。
- 适合关注时序分析、智能决策系统的研究者与开发者。
自动识别实体是否被引用在未来语境中，可应用于决策、规划和趋势预测等多个场景。本文聚焦于实体中心文本中隐含的未来参考检测，回应信息处理中日益增长的自动化时间分析需求。首先，我们构建了一个包含19,540个句子的新数据集，围绕维基百科中的热门实体，涵盖这些实体出现的未来相关与非未来相关语境。其次，我们评估了包括大型语言模型（LLMs）在内的多种语言模型，在缺乏显式时间标记的情况下，区分未来导向内容的表现。
原文摘要 · Abstract (English)
The ability to automatically identify whether an entity is referenced in a future context can have multiple applications including decision making, planning and trend forecasting. This paper focuses on detecting implicit future references in entity-centric texts, addressing the growing need for automated temporal analysis in information processing. We first present a novel dataset of 19,540 sentences built around popular entities sourced from Wikipedia, which consists of future-related and non-future-related contexts in which those entities appear. As a second contribution, we evaluate the performance of several Language Models including also Large Language Models (LLMs) on the task of distinguishing future-oriented content in the absence of explicit temporal references.
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