Detecting Future-related Contexts of Entity Mentions
This addresses the need for automated temporal analysis in information processing, but it is incremental as it builds on existing language models for a specific domain.
The paper tackles the problem of detecting implicit future references for entities in text, creating a dataset of 19,540 sentences and evaluating language models on this task.
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.