2013
DOI: 10.1007/978-3-642-37256-8_37
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Explanation in Computational Stylometry

Abstract: Abstract. Computational stylometry, as in authorship attribution or profiling, has a large potential for applications in diverse areas: literary science, forensics, language psychology, sociolinguistics, even medical diagnosis. Yet, many of the basic research questions of this field are not studied systematically or even at all. In this paper we will go into these problems, and suggest that a reinterpretation of current and historical methods in the framework and methodology of machine learning of natural lang… Show more

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Cited by 71 publications
(68 citation statements)
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References 27 publications
(24 reference statements)
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“…The more observations we have available per text, the more trustworthily one can represent it. Character n-grams push this idea even further, simply because texts by definition have more data points for character n-grams than for entire words (Stamatatos, 2009;Daelemans, 2013). Thus the mere number of observations, relatively larger for character n-grams than for function words, might account for their superiority from a purely quantitative perspective.…”
Section: Character N-gramsmentioning
confidence: 99%
“…The more observations we have available per text, the more trustworthily one can represent it. Character n-grams push this idea even further, simply because texts by definition have more data points for character n-grams than for entire words (Stamatatos, 2009;Daelemans, 2013). Thus the mere number of observations, relatively larger for character n-grams than for function words, might account for their superiority from a purely quantitative perspective.…”
Section: Character N-gramsmentioning
confidence: 99%
“…Intelligent personal assistants such as Apple's Siri and Amazon's Alexa leverage machine learning techniques, including Deep Neural Networks (DNN), convolutional neural networks (Tang and Lin, 2017), long short-term memory units (Chen et al, 2015), gated recurrent units (Ravuri and Stolcke, 2016), and ngrams (Daelemans, 2013;Levy, 2016) to build smart voice recognition system. (Rybach et al, 2011).…”
Section: Speech Recognitionmentioning
confidence: 99%
“…Feature selection is most important rather than the choice of machine learning method (Daelemans, 2013). However, classifiers selection is highly related to feature extraction and selection.…”
Section: Feature Selection For Oathmentioning
confidence: 99%