Understanding relation temporality of entities

Abstract

This paper demonstrates the importance of relation equivalence for entity translation pair discovery. Existing approach of understanding relation equivalence has focused on using explicit features of cooccurring entities. In this paper, we explore latent features of temporality for understanding relation equivalence, and empirically show that the explicit and latent features complement each other. Our proposed hybrid approach of using both explicit and latent features improves relation translation by 0.16 F1-score, and in turn improves entity translation by 0.02. © 2014 Association for Computational Linguistics.