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Boosting word frequencies in authorship attribution

2022-11-02 17:11:35
Maciej Eder
arXiv_CL
     
Abstract

In this paper, I introduce a simple method of computing relative word frequencies for authorship attribution and similar stylometric tasks. Rather than computing relative frequencies as the number of occurrences of a given word divided by the total number of tokens in a text, I argue that a more efficient normalization factor is the total number of relevant tokens only. The notion of relevant words includes synonyms and, usually, a few dozen other words in some ways semantically similar to a word in question. To determine such a semantic background, one of word embedding models can be used. The proposed method outperforms classical most-frequent-word approaches substantially, usually by a few percentage points depending on the input settings.

Abstract (translated)

URL

https://arxiv.org/abs/2211.01289

PDF

https://arxiv.org/pdf/2211.01289.pdf

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