Sentence Level Temporality Detection using an Implicit Time-sensed Resource

Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

Abstract

Temporal sense detection of any word is an important aspect for detecting temporality at the sentence level. In this paper, at first, we build a temporal resource based on a semi-supervised learning approach where each Hindi-WordNet synset is classified into one of the five classes, namely past, present, future, neutral and atemporal. This resource is then utilized for tagging the sentences with past, present and future temporal senses. For the sentence-level tagging, we use a rule-based as well as a machine learning-based approach. We provide detailed analysis along with necessary resources.