{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:32:08Z","timestamp":1777703528379,"version":"3.51.4"},"reference-count":38,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2018,5,24]],"date-time":"2018-05-24T00:00:00Z","timestamp":1527120000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,5,24]]},"abstract":"<jats:p>\n                    Before the advent of the Internet era, code-mixing was mainly used in the spoken form. However, with the recent popular informal networking platforms such as Facebook, Twitter, Instagram, etc., in social media, code-mixing is being used more and more in written form. User-generated social media content is becoming an increasingly important resource in applied linguistics. Recent trends in social media usage have led to a proliferation of studies on social media content. Multilingual social media users often write native language content in non-native script (cross-script). Recently Banerjee et al. [\n                    <jats:xref ref-type=\"bibr\">9<\/jats:xref>\n                    ] introduced the code-mixed cross-script question answering research problem and reported that the ever increasing social media content could serve as a potential digital resource for less-computerized languages to build question answering systems. Question classification is a core task in question answering in which questions are assigned a class or a number of classes which denote the expected answer type(s). In this research work, we address the question classification task as part of the code-mixed cross-script question answering research problem. We combine deep learning framework with feature engineering to address the question classification task and enhance the state-of-the-art question classification accuracy by over 4% for code-mixed cross-script questions.\n                  <\/jats:p>","DOI":"10.3233\/jifs-169481","type":"journal-article","created":{"date-parts":[[2018,5,25]],"date-time":"2018-05-25T05:01:20Z","timestamp":1527224480000},"page":"2959-2969","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Code mixed cross script factoid question classification - A deep learning approach"],"prefix":"10.1177","volume":"34","author":[{"given":"Somnath","family":"Banerjee","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur\u00a0University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudip","family":"Naskar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur\u00a0University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Rosso","sequence":"additional","affiliation":[{"name":"PRHLT Research Center, Universitat Polit\u00e8cnica de Val\u00e8ncia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sivaji","family":"Bandyopadhyay","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur\u00a0University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,5,24]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"1","article-title":"Challenges in Designing Input Method Editors for Indian languages: The Role of Word-origin and Context","author":"Ahmed U.Z.","year":"2011","unstructured":"AhmedU.Z., BaliK., ChoudhuryM., SowmyaV.B., Challenges in Designing Input Method Editors for Indian languages: The Role of Word-origin and Context. 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