@inproceedings{perez-estruch-etal-2017-learning,
title = "Learning Multimodal Gender Profile using Neural Networks",
author = "P{\'e}rez Estruch, Carlos and
Paredes Palacios, Roberto and
Rosso, Paolo",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
month = sep,
year = "2017",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://doi.org/10.26615/978-954-452-049-6_075",
doi = "10.26615/978-954-452-049-6_075",
pages = "577--582",
abstract = "Gender identification in social networks is one of the most popular aspects of user profile learning. Traditionally it has been linked to author profiling, a difficult problem to solve because of the little difference in the use of language between genders. This situation has led to the need of taking into account other information apart from textual data, favoring the emergence of multimodal data. The aim of this paper is to apply neural networks to perform data fusion, using an existing multimodal corpus, the NUS-MSS data set, that (not only) contains text data, but also image and location information. We improved previous results in terms of macro accuracy (87.8{\%}) obtaining the state-of-the-art performance of 91.3{\%}.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="perez-estruch-etal-2017-learning">
<titleInfo>
<title>Learning Multimodal Gender Profile using Neural Networks</title>
</titleInfo>
<name type="personal">
<namePart type="given">Carlos</namePart>
<namePart type="family">Pérez Estruch</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Roberto</namePart>
<namePart type="family">Paredes Palacios</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Paolo</namePart>
<namePart type="family">Rosso</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ruslan</namePart>
<namePart type="family">Mitkov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Galia</namePart>
<namePart type="family">Angelova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>INCOMA Ltd.</publisher>
<place>
<placeTerm type="text">Varna, Bulgaria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Gender identification in social networks is one of the most popular aspects of user profile learning. Traditionally it has been linked to author profiling, a difficult problem to solve because of the little difference in the use of language between genders. This situation has led to the need of taking into account other information apart from textual data, favoring the emergence of multimodal data. The aim of this paper is to apply neural networks to perform data fusion, using an existing multimodal corpus, the NUS-MSS data set, that (not only) contains text data, but also image and location information. We improved previous results in terms of macro accuracy (87.8%) obtaining the state-of-the-art performance of 91.3%.</abstract>
<identifier type="citekey">perez-estruch-etal-2017-learning</identifier>
<identifier type="doi">10.26615/978-954-452-049-6_075</identifier>
<part>
<date>2017-09</date>
<extent unit="page">
<start>577</start>
<end>582</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Learning Multimodal Gender Profile using Neural Networks
%A Pérez Estruch, Carlos
%A Paredes Palacios, Roberto
%A Rosso, Paolo
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F perez-estruch-etal-2017-learning
%X Gender identification in social networks is one of the most popular aspects of user profile learning. Traditionally it has been linked to author profiling, a difficult problem to solve because of the little difference in the use of language between genders. This situation has led to the need of taking into account other information apart from textual data, favoring the emergence of multimodal data. The aim of this paper is to apply neural networks to perform data fusion, using an existing multimodal corpus, the NUS-MSS data set, that (not only) contains text data, but also image and location information. We improved previous results in terms of macro accuracy (87.8%) obtaining the state-of-the-art performance of 91.3%.
%R 10.26615/978-954-452-049-6_075
%U https://doi.org/10.26615/978-954-452-049-6_075
%P 577-582
Markdown (Informal)
[Learning Multimodal Gender Profile using Neural Networks](https://doi.org/10.26615/978-954-452-049-6_075) (Pérez Estruch et al., RANLP 2017)
ACL
- Carlos Pérez Estruch, Roberto Paredes Palacios, and Paolo Rosso. 2017. Learning Multimodal Gender Profile using Neural Networks. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 577–582, Varna, Bulgaria. INCOMA Ltd..