Publication: The Rise of Metaphorical Usage of Lilith: Análisis de la presencia de Lilith en el contexto histórico y en el contexto actual con técnicas de Minería de textos.
Loading...
Official URL
Full text at PDC
Publication Date
2022-09
Authors
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Citations
Exportar
Abstract
Description
UCM subjects
Unesco subjects
Keywords
Citation
David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993–1022, 2003.
Elizabeth Closs Traugott. On regularity in semantic change. 1985.
Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20(3): 273–297, 1995.
David R Cox. Two further applications of a model for binary regression. Biometrika, 45 (3/4):562–565, 1958.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint ar-
Xiv:1810.04805, 2018.
Adji B Dieng, Francisco J R Ruiz, and David M Blei. Topic modeling in embedding spaces. arXiv preprint arXiv: 1907.04907, 2019.
Golrokh Eetessam Párraga. Lilith y sus descendientes: trayectoria del mito de la"femme fatale.en las literaturas europeas. 2016.
Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. From data mining to knowledge discovery in databases. AI magazine, 17(3):37–37, 1996.
Ronen Feldman and Ido Dagan. Knowledge discovery in textual databases (kdt). In KDD, volume 95, pages 112–117, 1995.
Jerome H Friedman. Stochastic gradient boosting. Computational statistics & data analysis, 38(4):367–378, 2002.
Robert Graves, Raphael Patai, and Javier García-Gutiérrez. Los mitos hebreos. Alianza Madrid, 2000.
Thomas L Griffiths and Mark Steyvers. Finding scientific topics. Proceedings of the National academy of Sciences, 101(suppl_1):5228–5235, 2004.
Maarten Grootendorst. Bertopic: Neural topic modeling with a class-based tf-idf procedure.
arXiv preprint arXiv:2203.05794, 2022.
Pierre Guiraud. La semiología. Siglo XXI, 1979.
Tin Kam Ho. Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition, volume 1, pages 278–282. IEEE, 1995.
Arthur E Hoerl and Robert W Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1):55–67, 1970.
Andreas Hotho, Andreas Nürnberger, and Gerhard Paaß. A brief survey of text mining. In Ldv Forum, volume 20, pages 19–62. Citeseer, 2005.
Arias Rodríguez Iván, Fernández Pampillón Cesteros Ana, Samy Doaa, and Arús Hita Jorge. Taller sobre herramientas de análisis textual: La herramienta Sketch Engine. Facultad de Filología, Universidad Complutense de Madrid, 2020.
Miloš Jakubíček, Adam Kilgarriff, Vojtěch Kovář, Pavel Rychl`y, and Vít Suchomel. The tenten corpus family. In 7th International Corpus Linguistics Conference CL, pages
125–127. Lancaster University, 2013.
Miloš Jakubíček, Vojtěch Kovář, Pavel Rychl`y, and Vít Suchomel. Current challenges in web corpus building. Proceedings of the 12th Web as Corpus Workshop, pages 1–4,
2020.
Nathalie Japkowicz. The class imbalance problem: Significance and strategies. In Proc.of the Int’l Conf. on Artificial Intelligence, volume 56, pages 111–117. Citeseer, 2000.
Thorsten Joachims. Text categorization with support vector machines: Learning with many relevant features. In European conference on machine learning, pages 137–142. Springer, 1998.
Karen Sparck Jones. A statistical interpretation of term specificity and its application in
retrieval. Journal of documentation, 1972.
Winfred P Lehmann. Historical linguistics: an introduction. Routledge, 2013.
Chenliang Li, HaoranWang, Zhiqian Zhang, Aixin Sun, and Zongyang Ma. Topic modeling for short texts with auxiliary word embeddings. In Proceedings of the 39th International
ACM SIGIR conference on Research and Development in Information Retrieval, pages 165–174, 2016.
Arantzazú González López. El mito de lilith evolución iconográfica y conceptual. Revista Legado de Arquitectura y Diseño, (14):105–114, 2013.
MM Martínez-Oña and AM Muñoz-Muñoz. Iconographic analysis of the myth of lilith in advertising. Revista Latina de Comunicación Social, (70), 2015.
Andrew McCallum, Kamal Nigam, et al. A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization, volume 752, pages 41–48. Citeseer, 1998.
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
David Mimno, HannaWallach, Edmund Talley, Miriam Leenders, and Andrew McCallum. Optimizing semantic coherence in topic models. In Proceedings of the 2011 conference
on empirical methods in natural language processing, pages 262–272, 2011.
Tom M Mitchell and Tom M Mitchell. Machine learning, volume 1. McGraw-hill New York, 1997.
Kevin P Murphy. Machine learning: a probabilistic perspective. MIT press, 2012.
William E Nagy, Patricia A Herman, and Richard C Anderson. Learning words from context. Reading research quarterly, pages 233–253, 1985.
Juri Opitz and Sebastian Burst. Macro f1 and macro f1. arXiv preprint arXiv:1911.03347, 2019.
Golrokh Eetessam Párraga. Lilith en el arte decimonónico: estudio del mito de la femme fatale. Signa: Revista de la Asociación Española de Semiótica, 18, 2009.
Constituency Parsing. Speech and language processing. 2009.
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, 2014. URL http://www.aclweb.org/anthology/
D14-1162.
David MW Powers. Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061, 2020.
Payam Refaeilzadeh, Lei Tang, and Huan Liu. Cross-alidation. Encyclopedia of database systems, 5:532–538, 2009.
Michael Röder, Andreas Both, and Alexander Hinneburg. Exploring the space of topic coherence measures. In Proceedings of the eighth ACM international conference on Web search and data mining, pages 399–408, 2015.
Gerard Salton, Anita Wong, and Chung-Shu Yang. A vector space model for automatic indexing. Communications of the ACM, 18(11):613–620, 1975.
Carson Sievert and Kenneth Shirley. Ldavis: A method for visualizing and interpreting topics. In Proceedings of the workshop on interactive language learning, visualization,
and interfaces, pages 63–70, 2014.
Julia Silge and David Robinson. Text mining with R: A tidy approach. .O’Reilly Media, Inc.", 2017.
Stephen V Stehman. Selecting and interpreting measures of thematic classification accuracy.
Remote sensing of Environment, 62(1):77–89, 1997.
KL Sumathy and M Chidambaram. Text mining: concepts, applications, tools and issuesan
overview. International Journal of Computer Applications, 80(4), 2013.
Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267–288, 1996.
Stephen Ullmann. Semántica. Aguilar Madrid, 1965.
Hanna Wallach, David Mimno, and Andrew McCallum. Rethinking lda: Why priors matter. Advances in neural information processing systems, 22, 2009.
Guangxu Xun, Vishrawas Gopalakrishnan, Fenglong Ma, Yaliang Li, Jing Gao, and Aidong Zhang. Topic discovery for short texts using word embeddings. In 2016 IEEE 16th international conference on data mining (ICDM), pages 1299–1304. IEEE, 2016.
Harry Zhang. The optimality of naive bayes. Aa, 1(2):3, 2004.
Hui Zou and Trevor Hastie. Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology), 67(2):301–320,
2005