A Review On Authorship Attribution In Text Mining Pdf Part Of In this paper, we present a novel method that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures. the proposed method attempts to mask topic specific information that is not related to the personal style of authors. Pdf | on jan 1, 2017, efstathios stamatatos published authorship attribution using text distortion | find, read and cite all the research you need on researchgate.
Pdf Authorship Attribution Using Text Distortion In this paper, we present a novel method that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures. the proposed method attempts to mask topicspecific information that is not related to the personal style of authors. An especially challenging but very realistic scenario is cross domain attribution where texts of known authorship (training set) di er from texts of disputed authorship (test set) in topic or genre. The aim of this study is to address this question by means of a strictly controlled experiment of authorship attribution, with texts of known authorship about strictly controlled topics being analysed between and within genres as well as between and within authors. In this paper, a set of text distortion methods are used attempting to mask topic related information. these methods transform the input texts into a more topic neutral form while maintaining the structure of documents associated with the personal style of the author.
Github Adauchendu Authorship Attribution For Neural Text Generation The aim of this study is to address this question by means of a strictly controlled experiment of authorship attribution, with texts of known authorship about strictly controlled topics being analysed between and within genres as well as between and within authors. In this paper, a set of text distortion methods are used attempting to mask topic related information. these methods transform the input texts into a more topic neutral form while maintaining the structure of documents associated with the personal style of the author. In this paper, we present a novel method that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures. In this paper, we present a novel method that en hances authorship attribution effectiveness by introducing a text distortion step be fore extracting stylometric measures. the proposed method attempts to mask topic specific information that is not related to the personal style of authors. To this end, we will consider seven text knowledge sources, including a number of alternative text representations based on word, character and text distortion n grams (stamatatos, 2017), the use of morphological and syntactic information and word embeddings. In this paper, we propose a novel method that is based on text distortion to compress topic related information.
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