# A Factorized Probabilistic Model of the Semantics of Vague Temporal Adverbials Relative to Different Event Types

arXiv: https://arxiv.org/abs/2505.01311 · HTML: https://mlsanity.com/paper/2505.01311/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 2 May 2025.

## Builds on (most cited first, 1 of 1)

- [Learning Conceptual Space Representations of Interrelated Concepts](https://mlsanity.com/paper/1805.01276/) (2018)

## Built on by (most cited first, 1 of 1)

- [PAA: The Probabilistic Allen Algebra: A Generative and Complete Probabilistic Extension of Allen's Interval Relations](https://mlsanity.com/paper/2609.20634/) (2026)

## Abstract (arXiv)

Vague temporal adverbials, such as recently, just, and a long time ago, describe the temporal distance between a past event and the utterance time but leave the exact duration underspecified. In this paper, we introduce a factorized model that captures the semantics of these adverbials as probabilistic distributions. These distributions are composed with event-specific distributions to yield a contextualized meaning for an adverbial applied to a specific event. We fit the model's parameters using existing data capturing judgments of native speakers regarding the applicability of these vague temporal adverbials to events that took place a given time ago. Comparing our approach to a non-factorized model based on a single Gaussian distribution for each pair of event and temporal adverbial, we find that while both models have similar predictive power, our model is preferable in terms of Occam's razor, as it is simpler and has better extendability.