TempO-HindiWordNet: a lexical knowledge-base for temporal information processing

Sabyasachi Kamila, Mohammad Hasanuzzaman, Asif Ekbal, Pushpak Bhattacharyya

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Temporality has significantly contributed to various Natural Language Processing and Information Retrieval applications. In this article, we first create a lexical knowledge-base in Hindi by identifying the temporal orientation of word senses based on their definition and then use this resource to detect underlying temporal orientation of the sentences. To create the resource, we propose a semi-supervised learning framework, where each synset of the Hindi WordNet is classified into one of the five categories, namely, past, present, future, neutral, and atemporal. The algorithm initiates learning with a set of seed synsets and then iterates following different expansion strategies, viz. probabilistic expansion based on classifier's confidence and semantic distance based measures. We manifest the usefulness of the resource that we build on an external task, viz. sentence-level temporal classification. The underlying idea is that a temporal knowledge-base can help in classifying the sentences according to their inherent temporal properties. Experiments on two different domains, viz. general and Twitter, show interesting results.

Original languageEnglish
Article number19
Number of pages22
JournalACM Transactions on Asian and Low-Resource Language Information Processing
Issue number2
Early online date14 Dec 2018
Publication statusPublished - Jun 2019
Externally publishedYes

Bibliographical note

Funding Information:
Asif Ekbal acknowledges Young Faculty Research Fellowship (YFRF), supported by Visvesvaraya PhD scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia). Authors’ addresses: S. Kamila, A. Ekbal, and P. Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihar, India, 801106; emails: {sabysachi.pcs16, asif, pb}@iitp.ac.in; M. Hasanuzzaman, School of Computing, Dublin City University, Dublin, Ireland; email: hasanuzzaman.im@gmail.com. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2018 Association for Computing Machinery. 2375-4699/2018/12-ART19 $15.00 https://doi.org/10.1145/3277504

Publisher Copyright:
© 2018 Association for Computing Machinery.


  • Hindi
  • Semi-supervised machine learning
  • Sentence-level temporality detection
  • Temporal sense detection

ASJC Scopus subject areas

  • General Computer Science


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