Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs
by
Helena Gómez-Adorno 1,*, Grigori Sidorov 1, David Pinto 2, Darnes Vilariño 2 and Alexander Gelbukh 1
Helena Gómez-Adorno 1,*, Grigori Sidorov 1, David Pinto 2, Darnes Vilariño 2 and Alexander Gelbukh 1
1
Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz S/N, Mexico City 07738, Mexico
2
Benemérita Universidad Autónoma de Puebla, Facultad de Ciencias de la Computación, Av. San Claudio y 14 Sur, Puebla 72570, Mexico,
*
Author to whom correspondence should be addressed.
Academic Editor: Miguel González-Mendoza
Sensors 2016, 16(9), 1374; https://doi.org/10.3390/s16091374
Received: 31 May 2016 / Revised: 31 July 2016 / Accepted: 19 August 2016 / Published: 29 August 2016
(This article belongs to the Special Issue Advances in Artificial Intelligence: Selected Papers from MICAI 2013, 2014 and 2015—12th, 13th and 14th Mexican International Conferences on Artificial Intelligence)
We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution.
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Keywords:
integrated syntactic graphs; textual patterns; authorship attribution; authorship verification; shortest paths walks; syntactic n-grams
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MDPI and ACS Style
Gómez-Adorno, H.; Sidorov, G.; Pinto, D.; Vilariño, D.; Gelbukh, A. Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs. Sensors 2016, 16, 1374.
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