Document embedding approach for efficient authorship attribution

Authorship attribution has been well studied in terms of text classification with many diverse feature sets. However, finding topic independent features is hard and trained models with hand crafted features in one domain may not work in another domain. In this study we used a semi-supervised neural language model which is known as document embeddings for authorship attribution problem. This method showed significant improvements over bag-of-words representations in a well-known dataset.