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25.01.2018

Document embeddings learned on various types of n-grams for cross-topic authorship attribution

verfasst von: Helena Gómez-Adorno, Juan-Pablo Posadas-Durán, Grigori Sidorov, David Pinto

Erschienen in: Computing | Ausgabe 7/2018

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Abstract

Recently, document embeddings methods have been proposed aiming at capturing hidden properties of the texts. These methods allow to represent documents in terms of fixed-length, continuous and dense feature vectors. In this paper, we propose to learn document vectors based on n-grams and not only on words. We use the recently proposed Paragraph Vector method. These n-grams include character n-grams, word n-grams and n-grams of POS tags (in all cases with n varying from 1 to 5). We considered the task of Cross-Topic Authorship Attribution and made experiments on The Guardian corpus. Experimental results show that our method outperforms word-based embeddings and character n-gram based linear models, which are among the most effective approaches for identifying the writing style of an author.

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http://www.nltk.org/.

https://www.theguardian.com.

https://radimrehurek.com/gensim.

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Titel: Document embeddings learned on various types of n-grams for cross-topic authorship attribution
verfasst von: Helena Gómez-Adorno
Juan-Pablo Posadas-Durán
Grigori Sidorov
David Pinto
Publikationsdatum: 25.01.2018
Verlag: Springer Vienna
Erschienen in: Computing / Ausgabe 7/2018
Print ISSN: 0010-485X
Elektronische ISSN: 1436-5057
DOI: https://doi.org/10.1007/s00607-018-0587-8

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