{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T20:25:05Z","timestamp":1774124705588,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T00:00:00Z","timestamp":1588118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Paraphrase detection is important for a number of applications, including plagiarism detection, authorship attribution, question answering, text summarization, text mining in general, etc. In this paper, we give a performance overview of various types of corpus-based models, especially deep learning (DL) models, with the task of paraphrase detection. We report the results of eight models (LSI, TF-IDF, Word2Vec, Doc2Vec, GloVe, FastText, ELMO, and USE) evaluated on three different public available corpora: Microsoft Research Paraphrase Corpus, Clough and Stevenson and Webis Crowd Paraphrase Corpus 2011. Through a great number of experiments, we decided on the most appropriate approaches for text pre-processing: hyper-parameters, sub-model selection\u2014where they exist (e.g., Skipgram vs. CBOW), distance measures, and semantic similarity\/paraphrase detection threshold. Our findings and those of other researchers who have used deep learning models show that DL models are very competitive with traditional state-of-the-art approaches and have potential that should be further developed.<\/jats:p>","DOI":"10.3390\/info11050241","type":"journal-article","created":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T13:23:45Z","timestamp":1588166625000},"page":"241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Corpus-Based Paraphrase Detection Experiments and Review"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4934-255X","authenticated-orcid":false,"given":"Tedo","family":"Vrbanec","sequence":"first","affiliation":[{"name":"Faculty of Teacher Education, University of Zagreb, Savska Cesta 77, 10000 Zagreb, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9513-9467","authenticated-orcid":false,"given":"Ana","family":"Me\u0161trovi\u0107","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Rijeka, Radmile Matej\u010di\u0107 2, 51000 Rijeka, Croatia"},{"name":"Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Radmile Matej\u010di\u0107 2, 51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1016\/j.ipm.2018.06.005","article-title":"A Deep Network Model for Paraphrase Detection in Short Text Messages","volume":"54","author":"Agarwal","year":"2017","journal-title":"Inf. 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