Boosting word frequencies in authorship attribution
This is an incremental improvement for stylometric tasks like authorship attribution.
The paper tackles authorship attribution by proposing a new method for computing relative word frequencies that normalizes by the total number of semantically relevant tokens instead of all tokens, resulting in performance improvements of a few percentage points over classical approaches.
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.