Paper: Classifying Temporal Relations with Simple Features

ACL ID E14-1033
Title Classifying Temporal Relations with Simple Features
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014

Approaching temporal link labelling as a classification task has already been ex- plored in several works. However, choos- ing the right feature vectors to build the classification model is still an open is- sue, especially for event-event classifica- tion, whose accuracy is still under 50%. We find that using a simple feature set re- sults in a better performance than using more sophisticated features based on se- mantic role labelling and deep semantic parsing. We also investigate the impact of extracting new training instances using in- verse relations and transitive closure, and gain insight into the impact of this boot- strapping methodology on classifying the full set of TempEval-3 relations.