A Factorized Probabilistic Model of the Semantics of Vague Temporal Adverbials Relative to Different Event Types
This addresses a specific linguistic modeling problem for computational semantics, with incremental improvements in simplicity and extendability.
The paper tackles the problem of modeling the semantics of vague temporal adverbials like 'recently' by introducing a factorized probabilistic model that combines adverbial and event-specific distributions, finding it simpler and more extendable than a non-factorized baseline with similar predictive power.
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.