Can Correspondence Analysis Challenge Transformers in Authorship Attribution Tasks?

With reference to a large corpus of 76 Italian contemporary popular mystery novels by 16 different authors, this study aims to assess the performance of large language models in an authorship attribution test. The results obtained through both transformers and correspondence analysis vector representations are compared and contrast in machine learning classification tasks. Although in previous works transformers have been shown to perform better than other alternatives, in this case, correspondence analysis wins the challenge. Results support the hypothesis that specialized large corpora require tailor-made representations.