{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:33:28Z","timestamp":1750221208236,"version":"3.41.0"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2018,12,14]],"date-time":"2018-12-14T00:00:00Z","timestamp":1544745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Government of India, being implemented by Digital India Corporation"},{"name":"Young Faculty Research Fellowship"},{"name":"Visvesvaraya PhD scheme for Electronics and IT, Ministry of Electronics and Information Technology"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2019,6,30]]},"abstract":"<jats:p>\n            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,\n            <jats:italic>past<\/jats:italic>\n            ,\n            <jats:italic>present<\/jats:italic>\n            ,\n            <jats:italic>future<\/jats:italic>\n            ,\n            <jats:italic>neutral<\/jats:italic>\n            , and\n            <jats:italic>atemporal<\/jats:italic>\n            . The algorithm initiates learning with a set of seed synsets and then iterates following different expansion strategies,\n            <jats:italic>viz.<\/jats:italic>\n            probabilistic expansion based on classifier\u2019s confidence and semantic distance based measures. We manifest the usefulness of the resource that we build on an external task,\n            <jats:italic>viz.<\/jats:italic>\n            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,\n            <jats:italic>viz.<\/jats:italic>\n            general and Twitter, show interesting results.\n          <\/jats:p>","DOI":"10.1145\/3277504","type":"journal-article","created":{"date-parts":[[2018,12,14]],"date-time":"2018-12-14T13:19:17Z","timestamp":1544793557000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Tempo-HindiWordNet"],"prefix":"10.1145","volume":"18","author":[{"given":"Sabyasachi","family":"Kamila","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Hasanuzzaman","sequence":"additional","affiliation":[{"name":"School of Computing, Dublin City University, Dublin, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asif","family":"Ekbal","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pushpak","family":"Bhattacharyya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,12,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1348\/135910708X299664"},{"volume-title":"Proceedings of the 1st International Temporal Web Analytics Workshop (TWAW\u201911)","year":"2011","author":"Alonso Omar","key":"e_1_2_1_2_1"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2348283.2348318"},{"volume-title":"IndoWordNet. 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