Authorship Attribution Using Word Sequences

Coyotl-Morales, Rosa María; Villaseñor-Pineda, Luis; Montes-y-Gómez, Manuel; Rosso, Paolo

doi:10.1007/11892755_87

Cited by 61 publications

(27 citation statements)

References 6 publications

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“…To take advantage of contextual information, word n-grams (n contiguous words aka word collocations) have been proposed as textual features (Peng, et al, 2004;Sanderson & Guenther, 2006;CoyotlMorales, Villaseñor-Pineda, Montes-y-Gómez, & Rosso, 2006). However, the classification accuracy achieved by word n-grams is not always better than individual word features (Sanderson & Guenther, 2006;Coyotl-Morales, et al, 2006). The dimensionality of the problem following this approach increases considerably with n to account for all the possible combinations between words.…”

Section: Lexical Featuresmentioning

confidence: 99%

A survey of modern authorship attribution methods

Stamatatos

2008

J. Am. Soc. Inf. Sci.

1,063

822

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Authorship attribution supported by statistical or computational methods has a long history starting from 19th century and marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed Federalist Papers. During the last decade, this scientific field has been developed substantially taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e-mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology provided it is able to handle short and noisy text from multiple candidate authors. In this paper, a survey of recent advances of the automated approaches to attributing authorship is presented examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area.

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Section: Lexical Featuresmentioning

confidence: 99%

A survey of modern authorship attribution methods

Stamatatos

2008

J. Am. Soc. Inf. Sci.

1,063

822

View full text Add to dashboard Cite

show abstract

“…Word n-grams can represent local structure of texts and document topic (Coyotl-Morales et al, 2006;Wang and Manning, 2012). On the other hand, character n-grams have been shown to be effective for capturing stylistic and morphological information (Koppel et al, 2011;Sapkota et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Continuous N-gram Representations for Authorship Attribution

Sari¹,

Vlachos²,

Stevenson³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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This paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.

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“…We use the corpus of contemporary Mexican poets used in [19]. This corpus was gathered from the Web.…”

Section: A Experimental Setupmentioning

confidence: 99%

Using Unlabeled Data to Improve Author Identification

Guzmán-Cabrera¹,

Sepúlveda²,

Sosa³

et al. 2013

IJCCE

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Abstract-Authorship attribution may be considered as a text categorization problem. Text categorization requires a large number of training examples which are particularly difficult to obtain in the case of authorship attribution task. In this paper, we investigate the possibility of using Web-based text-mining methods for the identification of the author of a given poem. In particular, we propose a semi-supervised method that is specially suited to work with just few training examples in order to tackle the problem of the lack of data with the same writing style. The results obtained on poem categorization show that this method may significantly improve the classification accuracy and it is appropriate to handle the attribution of short documents.

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Authorship Attribution Using Word Sequences

Cited by 61 publications

References 6 publications

A survey of modern authorship attribution methods

A survey of modern authorship attribution methods

Continuous N-gram Representations for Authorship Attribution

Using Unlabeled Data to Improve Author Identification

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