{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T22:59:00Z","timestamp":1762642740936,"version":"3.38.0"},"reference-count":44,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T00:00:00Z","timestamp":1660176000000},"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 Information Science"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:p> The scientific community has reacted to the COVID-19 outbreak by producing a high number of literary works that are helping us to understand a variety of topics related to the pandemic from different perspectives. Dealing with this large amount of information can be challenging, especially when researchers need to find answers to complex questions about specific topics. We present an Information Retrieval System that uses latent information to select relevant works related to specific concepts. By applying Latent Dirichlet Allocation (LDA) models to documents, we can identify key concepts related to a specific query and a corpus. Our method is iterative in that, from an initial input query defined by the user, the original query is expanded for each subsequent iteration. In addition, our method is able to work with a limited amount of information per article. We have tested the performance of our proposal using human validation and two evaluation strategies, achieving good results in both of them. Concerning the first strategy, we performed two surveys to determine the performance of our model. For all the categories that were studied, precision was always greater than 0.6, while accuracy was always greater than 0.8. The second strategy also showed good results, achieving a precision of 1.0 for one category and scoring over 0.7 points overall. <\/jats:p>","DOI":"10.1177\/01655515221110995","type":"journal-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T06:02:11Z","timestamp":1660284131000},"page":"935-951","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Finding answers to COVID-19-specific questions: An information retrieval system based on latent keywords and adapted TF-IDF"],"prefix":"10.1177","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6334-3786","authenticated-orcid":false,"given":"Jorge","family":"Chamorro-Padial","sequence":"first","affiliation":[{"name":"CITIC-UGR, Universidad de Granada, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6235-6860","authenticated-orcid":false,"given":"Francisco-Javier","family":"Rodrigo-Gin\u00e9s","sequence":"additional","affiliation":[{"name":"NLP & IR Group, UNED, Spain"}]},{"given":"Rosa","family":"Rodr\u00edguez-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias de la Computaci\u00f3n e Inteligencia Artificial, CITIC-UGR, Universidad de Granada, Spain"}]}],"member":"179","published-online":{"date-parts":[[2022,8,11]]},"reference":[{"key":"bibr1-01655515221110995","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijid.2020.01.009"},{"key":"bibr2-01655515221110995","unstructured":"World Health Organization (WHO). WHO Director-General\u2019s opening remarks at the media briefing on COVID-19 \u2013 6 March 2020, https:\/\/www.who.int\/dg\/speeches\/detail\/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19-6-march-2020 (2020, accessed 26 May 2020)."},{"key":"bibr3-01655515221110995","first-page":"e7357","volume":"12","author":"Chahrour M","year":"2020","journal-title":"Cureus"},{"key":"bibr4-01655515221110995","doi-asserted-by":"publisher","DOI":"10.3386\/w26867"},{"key":"bibr5-01655515221110995","volume":"17","author":"Li S","year":"2020","journal-title":"Int J Environ Res Public Health"},{"key":"bibr6-01655515221110995","doi-asserted-by":"publisher","DOI":"10.2196\/19118"},{"key":"bibr7-01655515221110995","first-page":"758","volume":"40","author":"Huynh TLD","year":"2020","journal-title":"Econ Bullet"},{"key":"bibr8-01655515221110995","doi-asserted-by":"publisher","DOI":"10.3145\/epi.2020.mar.15."},{"key":"bibr9-01655515221110995","unstructured":"COVID-19 open research dataset challenge (CORD-19)| Kaggle, https:\/\/www.kaggle.com\/allen-institute-for-ai\/CORD-19-research-challenge (accessed 30 January 2022)."},{"key":"bibr10-01655515221110995","first-page":"3411","volume":"24","author":"Lou J","year":"2020","journal-title":"Eur Rev Med Pharmacol Sci"},{"key":"bibr11-01655515221110995","doi-asserted-by":"publisher","DOI":"10.1101\/2020.03.19.20038752."},{"key":"bibr12-01655515221110995","doi-asserted-by":"publisher","DOI":"10.1080\/17538068.2020.1858222"},{"key":"bibr13-01655515221110995","doi-asserted-by":"publisher","DOI":"10.3390\/su13126534"},{"key":"bibr14-01655515221110995","unstructured":"Feng Y, Zhou W. 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