TempoWordNet for sentence time tagging

Gaël Harry Dias, Mohammed Hasanuzzaman, Stéphane Ferrari, Yann Mathet

Abstract: In this paper, we propose to build a temporal ontology, which may contribute to the success of time-related applications. Temporal classifiers are learned from a set of time-sensitive synsets and then applied to the whole WordNet to give rise to TempoWordNet. So, each synset is augmented with its intrinsic temporal value. To evaluate TempoWordNet, we use a semantic vector space representation for sentence temporal classification, which shows that improvements may be achieved with the time-augmented knowledge base against a bag-of-ngrams representation.