Author Profiling in Informal and Formal Language Scenarios Via Transfer Learning

Escobar-Grisales, Daniel; Vásquez-Correa, Juan Camilo; Orozco-Arroyave, Juan Rafael

Author Profiling in Informal and Formal Language Scenarios Via Transfer Learning

dc.creator	Escobar-Grisales, Daniel
dc.creator	Vásquez-Correa, Juan Camilo
dc.creator	Orozco-Arroyave, Juan Rafael
dc.date	2021-12-17
dc.date.accessioned	2025-10-01T23:52:46Z
dc.description	The interest in author profiling tasks has increased in the research community because computer applications have shown success in different sectors such as security, marketing, healthcare, and others. Recognition and identification of traits such as gender, age or location based on text data can help to improve different marketing strategies. This type of technology has been widely discussed regarding documents taken from social media. However, its methods have been poorly studied using data with a more formal structure, where there is no access to emoticons, mentions, and other linguistic phenomena that are only present in social media. This paper proposes the use of recurrent and convolutional neural networks and a transfer learning strategy to recognize two demographic traits, i.e., gender and language variety, in documents written in informal and formal language. The models were tested in two different databases consisting of tweets (informal) and call-center conversations (formal). Accuracies of up to 75 % and 68 % were achieved in the recognition of gender in documents with informal and formal language, respectively. Moreover, regarding language variety recognition, accuracies of 92 % and 72 % were obtained in informal and formal text scenarios, respectively. The results indicate that, in relation to the traits considered in this paper, it is possible to transfer the knowledge from a system trained on a specific type of expressions to another one where the structure is completely different and data are scarcer.	en-US
dc.description	El interés en tareas de perfilamiento de autor ha aumentado en la comunidad científica porque las aplicaciones han mostrado éxito en diferentes sectores como la seguridad, el mercadeo, la salud, entre otros. El reconocimiento e identificación de rasgos como el género, la edad, el dialecto o la personalidad a partir de datos de texto puede ayudar a mejorar diferentes estrategias de mercadeo. Este tipo de tecnología ha sido ampliamente discutida considerando documentos de redes sociales. Sin embargo, los métodos han sido pobremente estudiados en datos con una estructura más formal, donde no se tiene acceso a emoticones, menciones, y otros fenómenos lingüísticos que solo están presentes en redes sociales. Este trabajo propone el uso de redes neuronales recurrentes y convolucionales, y una estrategia de transferencia de aprendizaje para reconocer dos rasgos demográficos: el género y la variedad lingüística en documentos que están escritos en lenguajes informales y formales. Los modelos se prueban en dos bases de datos diferentes que consisten en Tuits (informal) y conversaciones de centros de llamadas (formal). Se obtienen precisiones del 75 % y del 68 % para el reconocimiento de género en documentos con una estructura informal y formal, respectivamente. Además, para el reconocimiento de variedad lingüística se obtuvieron precisiones del 92 % y del 72 % en documentos con una estructura informal y formal, respectivamente. Los resultados indican que, para los rasgos considerados, es posible transferir el conocimiento de un sistema entrenado en un tipo específico de expresiones a otro, donde la cantidad de datos es más escasa y su estructura es completamente diferente.	es-ES
dc.format	application/pdf
dc.format	application/zip
dc.format	text/xml
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dc.identifier	https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2166
dc.identifier	10.22430/22565337.2166
dc.identifier.uri	https://hdl.handle.net/20.500.12622/7809
dc.language	eng
dc.publisher	Instituto Tecnológico Metropolitano (ITM)	es-ES
dc.relation	https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2166/2215
dc.relation	https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2166/2228
dc.relation	https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2166/2229
dc.relation	https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2166/2250
dc.relation	/ref/F. Chiu Hsieh; R. F. Sandroni Dias; I. Paraboni, “Author profiling from Facebook corpora,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), pp. 2566- 2570, 2018. https://aclanthology.org/L18-1407.pdf
dc.relation	/ref/O. Dogan; B. Oztaysi, “Gender prediction from classified indoor customer paths by fuzzy C-medoids clustering,” in Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham., pp. 160–169. https://doi.org/10.1007/978-3-030-23756-1_21
dc.relation	/ref/R. Hirt; N. Kühl; G. Satzger, “Cognitive computing for customer profiling: meta classification for gender prediction,” Electron. Mark., vol. 39, no. 1, pp. 93–106, Feb. 2019. https://doi.org/10.1007/s12525-019-00336-z
dc.relation	/ref/D. Fernandez-Lanvin; J. de Andres-Suarez; M. Gonzalez-Rodriguez; B. Pariente-Martinez, “The dimension of age and gender as user model demographic factors for automatic personalization in e-commerce sites,” Comput. Stand. Interfaces, vol. 59, pp. 1–9, Aug. 2018. https://doi.org/10.1016/j.csi.2018.02.001
dc.relation	/ref/M. Arroju; A. Hassan; G. Farnadi, “Age, gender and personality recognition using tweets in a multilingual setting Notebook for PAN at CLEF 2015”. in 6th Conference and Labs of the Evaluation Forum (CLEF), 2015, pp. 23-31. https://biblio.ugent.be/publication/7100086
dc.relation	/ref/A. Nemati, “Gender and Age Prediction Multilingual Author Profiles Based on Comments”. in FIRE (Working Notes), 2018. http://ceur-ws.org/Vol-2266/T4-4.pdf
dc.relation	/ref/P. Mishra; M. Del Tredici; H. Yannakoudakis; E. Shutova, “Author profiling for abuse detection”. in Proceedings of the 27th international conference on computational linguistics, 2018. https://aclanthology.org/C18-1093.pdf
dc.relation	/ref/B. G. Gebre; M. Zampieri; P. Wittenburg; T. Heskes, “Improving native language identification with TF-IDF weighting”. in Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications, 2013, pp. 216-223. http://hdl.handle.net/11858/00-001M-0000-000E-FB4D-B
dc.relation	/ref/K. M. Alomari; H. M. ElSherif; K. Shaalan, “Arabic tweets sentimental analysis using machine learning”. in Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science, vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_66
dc.relation	/ref/I. Markov; H. Gómez-Adorno; G. Sidorov, “Language-and subtask-dependent feature selection and classifier parameter tuning for author profiling Notebook for PAN at CLEF 2017,” CLEF (Working Notes), 2017. https://www.researchgate.net/profile/Ilia-Markov/publication/318501982_Language-_and_Subtask-Dependent_Feature_Selection_and_Classifier_Parameter_Tuning_for_Author_Profiling/links/596e040d0f7e9bd5f75f5d36/Language-and-Subtask-Dependent-Feature-Selection-and-Classifier-Parameter-Tuning-for-Author-Profiling.pdf
dc.relation	/ref/M. Martinc; I. Skrjanec; K. Zupan; S. Pollak, “PAN 2017: Author profiling-gender and language variety prediction,” in CLEF (Working Notes), 2017. https://pan.webis.de/downloads/publications/papers/martinc_2017.pdf
dc.relation	/ref/F. Rangel; P. Rosso; M. Potthast; B. Stein, “Overview of the 5th author profiling task at PAN 2017: Gender and language variety identification in twitter,” in Working notes papers of the CLEF, pp. 1613–0073, 2017. http://personales.upv.es/prosso/resources/RangelEtAl_PAN17.pdf
dc.relation	/ref/A. Basile; G. Dwyer; M. Medvedeva; J. Rawee; H. Haagsma; M. Nissim, “N-gram: New Groningen author-profiling model,” Jul. 2017. https://arxiv.org/abs/1707.03764
dc.relation	/ref/M. Potthast; T. Gollub; F. Rangel; P. Rosso; E. fstathios Stamatatos; B. Stein, “Improving the reproducibility of PAN’s shared tasks,” in Information Access Evaluation. Multilinguality, Multimodality, and Interaction. CLEF 2014. Lecture Notes in Computer Science, vol 8685. Springer, Cham, 2014, pp. 268–299. https://doi.org/10.1007/978-3-319-11382-1_22
dc.relation	/ref/M. L. Newman; C. J. Groom; L. D. Handelman; J. W. Pennebaker, “Gender differences in language use: An analysis of 14,000 text samples,” Discourse Processes, vol. 45, no. 3, pp. 211–236, Jun. 2008. https://doi.org/10.1080/01638530802073712
dc.relation	/ref/D. Rao; D. Yarowsky; A. Shreevats; M. Gupta, “Classifying latent user attributes in twitter,” in Proceedings of the 2nd international workshop on Search and mining user-generated contents - SMUC ’10, 2010, pp. 37–44. https://doi.org/10.1145/1871985.1871993
dc.relation	/ref/H. A. Schwartz et al., “Personality, gender, and age in the language of social media: The open-vocabulary approach,” PloS one, vol. 8, no. 9, e73791, Sep. 2013. https://doi.org/10.1371/journal.pone.0073791
dc.relation	/ref/W. Li; M. Dickinson, “Gender prediction for Chinese social media data,” in Proceedings of Recent Advances in Natural Language Processing, Varna, Bulgaria, 2017, pp. 438–445. https://doi.org/10.26615/978-954-452-049-6_058
dc.relation	/ref/M. Franco-Salvador; G. Kondrak; P. Rosso, “Bridging the native language and language variety identification tasks”, Procedia Computer Science, vol.112, pp. 1554–1561, 2017. https://doi.org/10.1016/j.procs.2017.08.068
dc.relation	/ref/M. E. Aragón; A. P. López-Monroy, “Author profiling and aggressiveness detection in Spanish tweets: Mex-a3t 2018,” in IberEval@SEPLN, 2018, pp. 134–139.
dc.relation	/ref/Y. Kim, “Convolutional neural networks for sentence classification,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, 2014, pp. 1746–1751. https://doi.org/10.3115/v1/D14-1181
dc.relation	/ref/N. Kalchbrenner; E. Grefenstette; P. Blunsom, “A convolutional neural network for modelling sentences,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, 2014, pp. 655–665. https://doi.org/10.3115/v1/P14-1062
dc.relation	/ref/N. Majumder; S. Poria; A. Gelbukh; E. Cambria, “Deep learning-based document modeling for personality detection from text,” IEEE Intell. Syst., vol. 32, no. 2, pp. 74–79, Mar. 2017. https://doi.org/10.1109/mis.2017.23
dc.relation	/ref/S. Ruder; P. Ghaffari; J. Breslin, “Character-level and multi-channel convolutional neural networks for large-scale authorship attribution,” ArXiv, Sep. 2016. https://arxiv.org/abs/1609.06686
dc.relation	/ref/H. Gómez-Adorno et al., “A convolutional neural network approach for gender and language variety identification,” J. Intell. Fuzzy Syst., vol. 36, no. 5, pp. 4845–4855, May. 2019. https://doi.org/10.3233/JIFS-179032
dc.relation	/ref/D. Kodiyanet, “Author profiling with bidirectional RNNs using attention with GRUs,” Notebook for PAN at CLEF 2017. https://web.archive.org/web/20181102143341id_/https://digitalcollection.zhaw.ch/bitstream/11475/1865/1/kodiyan17-notebook.pdf
dc.relation	/ref/J. V. Lochter; R. M. Silva; T. A. Almeida, “Deep learning models for representing out-of-vocabulary words”. in Brazilian Conference on Intelligent Systems. Springer, Cham, 2020, pp. 418-434. https://doi.org/10.1007/978-3-030-61377-8_29
dc.relation	/ref/M. González Bermúdez, “An analysis of twitter corpora and the differences between formal and colloquial tweets,” in Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, pp. 3153–3159. https://upcommons.upc.edu/handle/2117/79542
dc.relation	/ref/J. Gu; Z. Yu, “Data annealing for informal language understanding tasks,” arXiv, Apr. 2020. https://arxiv.org/abs/2004.13833
dc.relation	/ref/M. Potthast, F. Rangel; M. Tschuggnall; E. Stamatatos; P. Rosso; B. Stein, “Overview of PAN’17”. in CLEF 2017: Experimental IR Meets Multilinguality, Multimodality, and Interaction, Springer, Cham, pp 275-290. https://doi.org/10.1007/978-3-319-65813-1_25
dc.relation	/ref/D. W. Otter; J. R. Medina; J. K. Kalita, “A survey of the usages of deep learning for natural language processing,” in IEEE Trans. Neural Networks Learn. Syst., vol. 32, no. 2, pp. 604-624, Feb. 2021. https://doi.org/10.1109/TNNLS.2020.2979670
dc.relation	/ref/A. Torfi; R. A. Shirvani; Y. Keneshloo; N. Tavvaf; E. A Fox, “Natural language processing advancements by deep learning: A survey.” ArXiv, Mar. 2020. https://arxiv.org/abs/2003.01200
dc.relation	/ref/L. Arras; G. Montavon; K. R. Müller; W. Samek, “Explaining recurrent neural network predictions in sentiment analysis,” proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Copenhagen, 2017. https://doi.org/10.18653/v1/W17-5221
dc.relation	/ref/S. Minaee; E. Azimi; A. Abdolrashidi, “Deep-sentiment: Sentiment analysis using ensemble of CNN and bi-LSTM models,” ArXiv, Apr. 2019. https://arxiv.org/abs/1904.04206
dc.relation	/ref/J. Trofimovich, “Comparison of neural network architectures for sentiment analysis of Russian tweets,” in Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference Dialogue, Moscow. 2016, pp. 50–59. http://www.dialog-21.ru/media/3380/arkhipenkoetal.pdf
dc.relation	/ref/V. Satopaa; J. Albrecht; D. Irwin; B. Raghavan, “Finding a" kneedle" in a haystack: Detecting knee points in system behavior,” in 31st International Conference on Distributed Computing Systems Workshops, Minneapolis, 2011, pp. 166-171. https://doi.org/10.1109/ICDCSW.2011.20
dc.rights	Derechos de autor 2021 TecnoLógicas	es-ES
dc.source	TecnoLógicas; Vol. 24 No. 52 (2021); e2166	en-US
dc.source	TecnoLógicas; Vol. 24 Núm. 52 (2021); e2166	es-ES
dc.source	2256-5337
dc.source	0123-7799
dc.subject	Author profiling,	en-US
dc.subject	Gender Recognition	en-US
dc.subject	Language variety recognition	en-US
dc.subject	Transfer learning	en-US
dc.subject	Natural language processing	en-US
dc.subject	Perfilamiento de autor	es-ES
dc.subject	Reconocimiento de género y variedad lingüística	es-ES
dc.subject	Transferencia de aprendizaje	es-ES
dc.subject	Procesamiento del lenguaje natural	es-ES
dc.title	Author Profiling in Informal and Formal Language Scenarios Via Transfer Learning	en-US
dc.title	Perfilamiento de autor en escenarios lingüísticos informales y formales mediante aprendizaje por transferencia	es-ES
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Research Papers	en-US
dc.type	Artículos de investigación	es-ES