Paper: Enriching Entity Translation Discovery using Selective Temporality

ACL ID P13-2036
Title Enriching Entity Translation Discovery using Selective Temporality
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

This paper studies named entity trans- lation and proposes ?selective temporal- ity? as a new feature, as using temporal features may be harmful for translating ?atemporal? entities. Our key contribution is building an automatic classifier to dis- tinguish temporal and atemporal entities then align them in separate procedures to boost translation accuracy by 6.1%.