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.
@InProceedings{KAMILA18.210, author = {Sabyasachi Kamila and Asif Ekbal and Pushpak Bhattacharyya}, title = "{Sentence Level Temporality Detection using an Implicit Time-sensed Resource}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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