Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts

We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obtained better results when performing the data prep...

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Detalles Bibliográficos
Autores principales: Gómez-Adorno, Helena, Markov, Ilia, Sidorov, Grigori, Posadas-Durán, Juan-Pablo, Sanchez-Perez, Miguel A., Chanona-Hernandez, Liliana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066026/
https://www.ncbi.nlm.nih.gov/pubmed/27795703
http://dx.doi.org/10.1155/2016/1638936
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author Gómez-Adorno, Helena
Markov, Ilia
Sidorov, Grigori
Posadas-Durán, Juan-Pablo
Sanchez-Perez, Miguel A.
Chanona-Hernandez, Liliana
author_facet Gómez-Adorno, Helena
Markov, Ilia
Sidorov, Grigori
Posadas-Durán, Juan-Pablo
Sanchez-Perez, Miguel A.
Chanona-Hernandez, Liliana
author_sort Gómez-Adorno, Helena
collection PubMed
description We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obtained better results when performing the data preprocessing using the developed lexical resource. The resource includes dictionaries of slang words, contractions, abbreviations, and emoticons commonly used in social media. Each of the dictionaries was built for the English, Spanish, Dutch, and Italian languages. The resource is freely available.
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spelling pubmed-50660262016-10-30 Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts Gómez-Adorno, Helena Markov, Ilia Sidorov, Grigori Posadas-Durán, Juan-Pablo Sanchez-Perez, Miguel A. Chanona-Hernandez, Liliana Comput Intell Neurosci Research Article We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obtained better results when performing the data preprocessing using the developed lexical resource. The resource includes dictionaries of slang words, contractions, abbreviations, and emoticons commonly used in social media. Each of the dictionaries was built for the English, Spanish, Dutch, and Italian languages. The resource is freely available. Hindawi Publishing Corporation 2016 2016-10-03 /pmc/articles/PMC5066026/ /pubmed/27795703 http://dx.doi.org/10.1155/2016/1638936 Text en Copyright © 2016 Helena Gómez-Adorno et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Gómez-Adorno, Helena
Markov, Ilia
Sidorov, Grigori
Posadas-Durán, Juan-Pablo
Sanchez-Perez, Miguel A.
Chanona-Hernandez, Liliana
Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts
title Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts
title_full Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts
title_fullStr Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts
title_full_unstemmed Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts
title_short Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts
title_sort improving feature representation based on a neural network for author profiling in social media texts
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066026/
https://www.ncbi.nlm.nih.gov/pubmed/27795703
http://dx.doi.org/10.1155/2016/1638936
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