Mostrar el registro sencillo del ítem

dc.contributor.authorSilva, Jesusspa
dc.contributor.authorGarcía, Silviaspa
dc.contributor.authorBinda, María Alejandraspa
dc.contributor.authorMarin Gonzalez, Fredyspa
dc.contributor.authorBarrios, Rosiospa
dc.contributor.authorLeon Castro, Bellanitspa
dc.date.accessioned2021-01-28T13:02:18Z
dc.date.available2021-01-28T13:02:18Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7788spa
dc.description.abstractThis paper presents a method for detecting an author’s profile using the following two elements: gender and age. This is based on a set of dialogues, written in two languages: English and Spanish, provided for Author Profiling competence within the evaluation forum "Uncovering Plagiarism, Authorship, and Social Software Misuse" (PAN2018). Counts of lexical, semantic, and syntactic characteristics are used to generate a two-phase classification system, which first classifies gender and then age. The results obtained show that, with the amount of data available, it is possible to characterize both the age and gender of an author with an accuracy greater than 50%. However, these values could be improved by having more evidence of information in the training data.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.sourceProcedia Computer Sciencespa
dc.subjectSupervised Classificationspa
dc.subjectPAN 2018spa
dc.subjectGenderspa
dc.subjectAgespa
dc.subjectRandom forestspa
dc.titleA method for detecting the profile of an authorspa
dc.typeArtículo de revistaspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050920305391#!spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.03.101spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.references1 Schler, J., Koppel, M., Argamon, S., Pennebaker, J.W.: Effects of age and gender on blogging. In: Computational Approaches to Analyzing Weblogs, Papers from the 2006 AAAI Spring Symposium, Technical Report SS-06-03, Stanford, California, USA, March 27-29, 2006. (2006) 199–205spa
dc.relation.references2 Argamon S., Koppel M., Pennebaker J.W., Schler J. Automatically profiling the author of an anonymous text Commun. ACM, 52 (2) (2009), pp. 119-123 (February)spa
dc.relation.references3 Peersman, C., Daelemans, W., Van Vaerenbergh, L.: Predicting age and gender in online social networks. In: Proceedings of the 3rd International Workshop on Search and Mining User-generated Contents. SMUC ‘11, New York, NY, USA, ACM (2011) 37–44spa
dc.relation.references4 Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: “how old do you think i am?”: A study of language and age in twitter. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media. ICWSM 2013 (2013)spa
dc.relation.references5 Rangel, F., Rosso, P.: Use of language and author profiling: Identification of gender and age. In: Proceedings of the 10th Workshop on Natural Language Processing and Cognitive Science (NLPCS-2013). (2013)spa
dc.relation.references6 Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Procee- dings of the International Conference on New Methods in Language Processing, Manchester, UK (1994)spa
dc.relation.references7 Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Human Language Technology Conference (HLT-NAACL 2003). (2003)spa
dc.relation.references8 Viloria A., Lis-Gutiérrez J.P., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018)spa
dc.relation.references9 De Werra D. An introduction to timetabling European Journal of Operational Research, 19 (2) (1985), pp. 151-162spa
dc.relation.references10 Obit, J.H., Ouelhadj, D., Landa-Silva, D., Vun, T.K., & Alfred, R.: Designing a multi- agent approach system for distributed course timetabling, pp. 103–108, doi:10.1109/HIS.2011.6122088 (2011)spa
dc.relation.references11 Lewis, M.R.R.: Metaheuristics for university course timetabling. Ph.D. Thesis, Napier University (2006)spa
dc.relation.references12 Deng, X., Zhang, Y., Kang, B., Wu, J., Sun, X., & Deng, Y.: An application of genetic al- gorithm for university course timetabling problem, pp. 2119-2122, doi:10.1109/CCDC.2011.5968555 (2011)spa
dc.relation.references13 Mahiba A.A., Durai C.A.D. Genetic algorithm with search bank strategies for universi- ty course timetabling problem Procedia Engineering, 38 (2012), pp. 253-263spa
dc.relation.references14 Soria-Alcaraz, J.A.; Carpio, J.M.; Puga, Hé.; Melin, P.; Terashima-Marn, H.; Reyes, L.spa
dc.relation.references15 C. Sotelo-Figueroa, M.A. Castillo, O.; Melin P., Pedrycz W., Kacprzyk J. Generic Memetic Algorithm for Course Timetabling ITC2007 Recent Advances on Hybrid Approaches for Designing Intelligent Systems, Springer (2014), pp. 481-492 vol. 547spa
dc.relation.references16 Nguyen, K., Lu, T., Le, T., & Tran, N.: Memetic algorithm for a university course timeta- bling problem. pp. 67-71, doi:10.1007/978-3-642-25899-2_10 (2011)spa
dc.relation.referencesAladag, C., & Hocaoglu, G.: A tabu search algorithm to solve a course timetabling prob- lem. Hacettepe journal of mathematics and statistics, pp. 53–64 (2007)spa
dc.relation.references18 Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program (report 826) (1989).spa
dc.relation.references19 Viloria Amelec, Lezama Omar Bonerge Pineda Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs Procedia Computer Science, 151 (2019), pp. 1201-1206spa
dc.relation.references20 Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernández, L., & Cali, E.G. (2018, June). Database performance tuning and query optimization. In International Conference on Data Mining and Big Data (pp. 3-11). Springer, Cham.spa
dc.relation.references21 Viloria Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-580spa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


Ficheros en el ítem

Thumbnail
Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

  • Artículos científicos [3120]
    Artículos de investigación publicados por miembros de la comunidad universitaria.

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 International
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International