Author profiling with doc2vec neural network-based document embeddings

Ilia Markov; Helena Gómez-Adorno; Juan Pablo Posadas-Durán; Grigori Sidorov; Alexander Gelbukh

doi:10.1007/978-3-319-62428-0_9

Author profiling with doc2vec neural network-based document embeddings

Ilia Markov, Helena Gómez-Adorno, Juan Pablo Posadas-Durán, Grigori Sidorov, Alexander Gelbukh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

34 Scopus citations

Abstract

To determine author demographics of texts in social media such as Twitter, blogs, and reviews, we use doc2vec document embeddings to train a logistic regression classifier. We experimented with age and gender identification on the PAN author profiling 2014–2016 corpora under both single- and cross-genre conditions. We show that under certain settings the neural network-based features outperform the traditional features when using the same classifier. Our method outperforms existing state of the art under some settings, though the current state-of-the-art results on those tasks have been quite weak.

Original language	English
Title of host publication	Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings
Editors	Obdulia Pichardo-Lagunas, Sabino Miranda-Jimenez
Publisher	Springer Verlag
Pages	117-131
Number of pages	15
ISBN (Print)	9783319624273
DOIs	https://doi.org/10.1007/978-3-319-62428-0_9
State	Published - 2017
Event	15th Mexican International Conference on Artificial Intelligence, MICAI 2016 - Cancun, Mexico Duration: 23 Oct 2016 → 28 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10062 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th Mexican International Conference on Artificial Intelligence, MICAI 2016
Country/Territory	Mexico
City	Cancun
Period	23/10/16 → 28/10/16

Keywords

Author profiling
Document embeddings
Machine learning
Neural networks
doc2vec

Access to Document

10.1007/978-3-319-62428-0_9

Cite this

Markov, I., Gómez-Adorno, H., Posadas-Durán, J. P., Sidorov, G., & Gelbukh, A. (2017). Author profiling with doc2vec neural network-based document embeddings. In O. Pichardo-Lagunas, & S. Miranda-Jimenez (Eds.), Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings (pp. 117-131). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10062 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-62428-0_9

Markov, Ilia ; Gómez-Adorno, Helena ; Posadas-Durán, Juan Pablo et al. / Author profiling with doc2vec neural network-based document embeddings. Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings. editor / Obdulia Pichardo-Lagunas ; Sabino Miranda-Jimenez. Springer Verlag, 2017. pp. 117-131 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "To determine author demographics of texts in social media such as Twitter, blogs, and reviews, we use doc2vec document embeddings to train a logistic regression classifier. We experimented with age and gender identification on the PAN author profiling 2014–2016 corpora under both single- and cross-genre conditions. We show that under certain settings the neural network-based features outperform the traditional features when using the same classifier. Our method outperforms existing state of the art under some settings, though the current state-of-the-art results on those tasks have been quite weak.",

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Markov, I, Gómez-Adorno, H, Posadas-Durán, JP , Sidorov, G & Gelbukh, A 2017, Author profiling with doc2vec neural network-based document embeddings. in O Pichardo-Lagunas & S Miranda-Jimenez (eds), Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10062 LNAI, Springer Verlag, pp. 117-131, 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancun, Mexico, 23/10/16. https://doi.org/10.1007/978-3-319-62428-0_9

Author profiling with doc2vec neural network-based document embeddings. / Markov, Ilia; Gómez-Adorno, Helena; Posadas-Durán, Juan Pablo et al.
Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings. ed. / Obdulia Pichardo-Lagunas; Sabino Miranda-Jimenez. Springer Verlag, 2017. p. 117-131 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10062 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Author profiling with doc2vec neural network-based document embeddings

AU - Markov, Ilia

AU - Gómez-Adorno, Helena

AU - Posadas-Durán, Juan Pablo

AU - Sidorov, Grigori

AU - Gelbukh, Alexander

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - To determine author demographics of texts in social media such as Twitter, blogs, and reviews, we use doc2vec document embeddings to train a logistic regression classifier. We experimented with age and gender identification on the PAN author profiling 2014–2016 corpora under both single- and cross-genre conditions. We show that under certain settings the neural network-based features outperform the traditional features when using the same classifier. Our method outperforms existing state of the art under some settings, though the current state-of-the-art results on those tasks have been quite weak.

AB - To determine author demographics of texts in social media such as Twitter, blogs, and reviews, we use doc2vec document embeddings to train a logistic regression classifier. We experimented with age and gender identification on the PAN author profiling 2014–2016 corpora under both single- and cross-genre conditions. We show that under certain settings the neural network-based features outperform the traditional features when using the same classifier. Our method outperforms existing state of the art under some settings, though the current state-of-the-art results on those tasks have been quite weak.

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KW - Document embeddings

KW - Machine learning

KW - Neural networks

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings

A2 - Pichardo-Lagunas, Obdulia

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T2 - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016

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ER -

Markov I, Gómez-Adorno H, Posadas-Durán JP , Sidorov G , Gelbukh A. Author profiling with doc2vec neural network-based document embeddings. In Pichardo-Lagunas O, Miranda-Jimenez S, editors, Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings. Springer Verlag. 2017. p. 117-131. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-62428-0_9

Author profiling with doc2vec neural network-based document embeddings

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