Detecting Future-related Contexts of Entity Mentions
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Abstract (arXiv)
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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References · 8
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
- 2022Forecasting Future World Events with Neural Networkscited by 63
- 2020ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Datacited by 43
- 2023Large Language Model Prediction Capabilities: Evidence from a Real-World Forecasting Tournamentcited by 24
- 2025Navigating Tomorrow: Reliably Assessing Large Language Models Performance on Future Event Predictioncited by 5
- 2025Analyzing the Role of Context in Forecasting with Large Language Modelscited by 3
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