Detecting Future-related Contexts of Entity Mentions

TL;DR

This paper introduces a new dataset and evaluates language models' ability to detect implicit future references in entity-centric texts, aiding automated temporal analysis for decision making and trend forecasting.

Contribution

The paper presents a novel dataset of 19,540 sentences with future and non-future contexts and assesses language models' effectiveness in identifying implicit future references.

Findings

Language models can distinguish future-related contexts without explicit temporal cues.

The dataset enables benchmarking of models on implicit future reference detection.

Evaluation results highlight strengths and limitations of current language models.

Abstract

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.