Boosting word frequencies in authorship attribution

• URL: http://arxiv.org/abs/2211.01289v1
• Date: Wed, 2 Nov 2022 17:11:35 GMT
• Title: Boosting word frequencies in authorship attribution
• Authors: Maciej Eder
• Abstract summary: I introduce a simple method of computing relative word frequencies for authorship attribution and similar stylometric tasks. The notion of relevant words includes synonyms and, usually, a few dozen other words in some ways semantically similar to a word in question. The proposed method outperforms classical most-frequent-word approaches substantially.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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