{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:23:51Z","timestamp":1777703031688,"version":"3.51.4"},"reference-count":34,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T00:00:00Z","timestamp":1557792000000},"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":[[2019,5,14]]},"abstract":"<jats:p>The constant increase in the production of scientific literature is making it very difficult for experts to keep up to date with the state-of-the-art knowledge in their fields. The use of Natural Language Processing (NLP) is becoming a necessary aid to tackle this challenge. In the NLP field, the task of measuring semantic similarity between two sentences plays a vital role. It is a cornerstone for tasks like Q&amp;A, Information Retrieval, Automatic Summarization, etc., and it is a crucial element in the ultimate goal of computers being able to decode what is conveyed in human language expression.<\/jats:p>\n                  <jats:p>Measuring Semantic Similarity (SS) in short texts has specific challenges. Because there are fewer words to be compared, the meaning contribution of each word is more relevant, and it is important to take into account the syntax\u2019s contribution to the composed meaning. In addition, the highly specific and specialized vocabulary \u2014 Microbial Transcriptional-Regulation\u2014implies the lack of massive training resources. Our approach has been to use an ensemble of similarity metrics including string, distributional, and knowledge-based metric and to combine the results of such analyses. We have trained and tested these methods in a similarity corpus developed in-house.<\/jats:p>\n                  <jats:p>\n                    The task has proved very challenging, and the ensemble strategy has proved to be a good approach. Even though there is still much room for improvement in the precision of our methods concerning the human evaluation, we have managed to improve them reaching a strong correlation (\n                    <jats:italic>\u03c1<\/jats:italic>\n                    \u00a0=\u00a00.700).\n                  <\/jats:p>","DOI":"10.3233\/jifs-179026","type":"journal-article","created":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T12:04:35Z","timestamp":1557835475000},"page":"4777-4786","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["In the pursuit of semantic similarity for literature on microbial transcriptional regulation"],"prefix":"10.1177","volume":"36","author":[{"given":"Oscar","family":"Lithgow-Serrano","sequence":"first","affiliation":[{"name":"Computational Genomics, Centro de Ciencias Gen\u00f3micas, Universidad Nacional Aut\u00f3noma de M\u00e9xico (UNAM), Morelos, M\u00e9xico"},{"name":"Instituto de Investigaciones en Matem\u00e1ticas Aplicadas y en Sistemas (IIMAS), Universidad Nacional Aut\u00f3noma de M\u00e9xico (UNAM), Ciudad de M\u00e9xico, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julio","family":"Collado-Vides","sequence":"additional","affiliation":[{"name":"Computational Genomics, Centro de Ciencias Gen\u00f3micas, Universidad Nacional Aut\u00f3noma de M\u00e9xico (UNAM), Morelos, M\u00e9xico"},{"name":"Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,5,14]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"ArborA. SemEval-Task 2: Semantic Textual Similarity English Spanish and Pilot on Interpretability. SemEval2015 (SemEval) 2015 pp. 252\u2013263."},{"issue":"6","key":"e_1_3_3_3_2","first-page":"242","article-title":"Frege in space: A program of compositional distributional semantics","volume":"9","author":"Baroni M.","year":"2014","unstructured":"BaroniM., BernardiR. and ZamparelliR., Frege in space: A program of compositional distributional semantics, Linguistic Issues in Language Technology9(6) (2014), 242\u2013346.","journal-title":"Linguistic Issues in Language Technology"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-4666-5888-2.ch746"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-2402"},{"key":"e_1_3_3_6_2","unstructured":"ClarkS. Vector Space Models of Lexical Meaning 2012 pp. 1\u201342."},{"key":"e_1_3_3_7_2","first-page":"9","volume-title":"Proceedings of the Third InternationalWorkshop on Paraphrasing (IWP2005)","author":"Dolan W.B.","year":"2005","unstructured":"DolanW.B. and BrockettC., Automatically Constructing a Corpus of Sentential Paraphrases, Proceedings of the Third InternationalWorkshop on Paraphrasing (IWP2005), 2005, pp. 9\u201316."},{"issue":"3","key":"e_1_3_3_8_2","first-page":"389","article-title":"A survey on semantic similarity measure","volume":"2","author":"Elavarasi S.A.","year":"2014","unstructured":"ElavarasiS.A., AkilandeswariJ. and MenagaK., A survey on semantic similarity measure, International Journal of Research in Advent Technology2(3) (2014), 389\u2013398.","journal-title":"International Journal of Research in Advent Technology"},{"key":"e_1_3_3_9_2","unstructured":"FirthJ.R. A synopsis of linguistic theory 1930-55. Studies in Linguistic Analysis (special volume of the Philological Society) 1957 pp. 195259:1\u201332."},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2012.09.017"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/793091"},{"key":"e_1_3_3_12_2","unstructured":"HarispeS. RanwezS. JanaqiS. and MontmainJ. Semantic Similarity from Natural Language and Ontology Analysis 2017."},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1080\/00437956.1954.11659520"},{"key":"e_1_3_3_14_2","unstructured":"JanaqiS. SebastienH. RanwezS. JanaqiS. and MontmainJ. Semantic Measures for the Comparison of Units of Language Concepts or Instances from Text and Knowledge Representation Analysis A Comprehensive Survey and a Technical Introduction to Knowledge-based Measures Using Semantic Graph Analysis book for a more 1(1) (2016)."},{"key":"e_1_3_3_15_2","doi-asserted-by":"crossref","unstructured":"KashyapA. HanL. YusR. SleemanJ. SatyapanichT. GandhiS. and FininT. Robust semantic text similarity using LSA machine learning and linguistic resources volume 50 SpringerNetherlands 2016.","DOI":"10.1007\/s10579-015-9319-2"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/11575832_13"},{"key":"e_1_3_3_17_2","first-page":"1188","volume-title":"International Conference on Machine Learning - ICML 2014","volume":"32","author":"Le Q.","year":"2014","unstructured":"LeQ. and MikolovT., Distributed Representations of Sentences and Documents, International Conference on Machine Learning - ICML 2014, 32, 2014, pp. 1188\u20131196."},{"key":"e_1_3_3_18_2","article-title":"A grammar-based semantic similarity algorithm for natural language sentences","volume":"2014","author":"Lee M.C.","year":"2014","unstructured":"LeeM.C., ChangJ.W. and HsiehT.C., A grammar-based semantic similarity algorithm for natural language sentences, Scientific World Journal2014 (2014).","journal-title":"Scientific World Journal"},{"key":"e_1_3_3_19_2","unstructured":"Lithgow-SerranoO.W. Gama-CastroS. Ishida-Guti\u00e9rrezC. Mej\u00eda-AlmonteC. Tierrafr\u00edaV. Mart\u00ednez-LunaS. Santos-ZavaletaA. Vel\u00e1zquez-Ram\u00edrezD. and Collado-VidesJ. Similarity corpus on microbial transcriptional regulation doi.org page 219014 2017."},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-5010"},{"key":"e_1_3_3_21_2","first-page":"1","article-title":"Distributed representations of words and phrases and their compositionality","author":"Mikolov T.","year":"2013","unstructured":"MikolovT., ChenK., CorradoG. and DeanJ., Distributed representations of words and phrases and their compositionality, Nips (2013), 1\u20139.","journal-title":"Nips"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/219717.219748"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1551-6709.2010.01106.x"},{"key":"e_1_3_3_24_2","unstructured":"MukakaM.M. Statistics Corner: A guide to appropriate use of Correlation coefficient in medical research 24(September) (2012) 69\u201371."},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_3_26_2","unstructured":"SchusterI. Probabilistic models of natural language semantics (August) 2016."},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/SUPERC.1992.236684"},{"key":"e_1_3_3_28_2","first-page":"1631","article-title":"Recursive deep models for semantic compositionality over a sentiment treebank","author":"Socher R.","year":"2013","unstructured":"SocherR., PerelyginA. and WuJ., Recursive deep models for semantic compositionality over a sentiment treebank, Proceedings of the\u2026, 2013, pp. 1631\u20131642.","journal-title":"Proceedings of the\u2026"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2017.07.002"},{"key":"e_1_3_3_30_2","doi-asserted-by":"crossref","unstructured":"TianR. OkazakiN. and InuiK. Learning Semantically and Additively Compositional Distributional Representations 2016.","DOI":"10.18653\/v1\/P16-1121"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W16-1628"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.2693"},{"key":"e_1_3_3_33_2","first-page":"1","article-title":"Probabilistic semantics for natural Language,(January)","author":"van Eijck J.","year":"2011","unstructured":"van EijckJ. and LappinS., Probabilistic semantics for natural Language,(January), Unpublished Manuscript (2011), 1\u201315.","journal-title":"Unpublished Manuscript"},{"key":"e_1_3_3_34_2","doi-asserted-by":"crossref","unstructured":"WongpakaranN. WongpakaranT. WeddingD. and GwetK.L. A comparison of Cohen\u2019 s Kappa and Gwet\u2019 s AC1 when calculating inter-rater reliability coefficients: A study conducted with personality disorder samples 2013 pp. 1\u20137.","DOI":"10.1186\/1471-2288-13-61"},{"issue":"3","key":"e_1_3_3_35_2","first-page":"307","article-title":"From computing with numbers to computing withwords","volume":"12","author":"Zadeh A","year":"2002","unstructured":"ZadehA, From computing with numbers to computing withwords, Int J Appl Math Comput Science12(3) (2002), 307\u2013324.","journal-title":"Int J Appl Math Comput Science"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179026","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-179026","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:37:39Z","timestamp":1777455459000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-179026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,14]]},"references-count":34,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2019,5,14]]}},"alternative-id":["10.3233\/JIFS-179026"],"URL":"https:\/\/doi.org\/10.3233\/jifs-179026","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,14]]}}}