Artificial Neural Network Techniques in Authorship Attribution
نویسندگان
چکیده
منابع مشابه
Authorship Attribution Using a Neural Network Language Model
In practice, training language models for individual authors is often expensive because of limited data resources. In such cases, Neural Network Language Models (NNLMs), generally outperform the traditional non-parametric N-gram models. Here we investigate the performance of a feed-forward NNLM on an authorship attribution problem, with moderate author set size and relatively limited data. We a...
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The basic assumption of quantitative authorship attribution is that the author of a text can be selected from a set of possible authors by comparing the values of textual measurements in that text to their corresponding values in each possible author’s writing sample. Over the past three centuries, many types of textual measurements have been proposed, but never before have the majority of thes...
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In this paper, we explore a set of novel features for authorship attribution of documents. These features are derived from a word network representation of natural language text. As has been noted in previous studies, natural language tends to show complex network structure at word level, with low degrees of separation and scale-free (power law) degree distribution. There has also been work on ...
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Authorship attribution, the science of inferring characteristics of the author from the characteristics of documents written by that author, is a problem with a long history and a wide range of application. Recent work in “non-traditional” authorship attribution demonstrates the practicality of automatically analyzing documents based on authorial style, but the state of the art is confusing. An...
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Authorship attribution assigns works of contentious authorship to their rightful owners solving cases of theft, plagiarism and authorship disputes in academia and industry. In this paper we investigate the application of information retrieval techniques to attribution of authorship of C source code. In particular, we explore novel methods for converting C code into documents suitable for retrie...
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ژورنال
عنوان ژورنال: Southeast Europe Journal of Soft Computing
سال: 2013
ISSN: 2233-1859
DOI: 10.21533/scjournal.v2i2.31