| Resumen |
Recently, document embeddings methods have been proposed aiming at capturing hidden properties of the texts. These methods allow to represent documents in terms of fixed-length, continuous and dense feature vectors. In this paper,we propose to learn document vectors based on n-grams and not only onwords.We use the recently proposed Paragraph Vector method. These n-grams include character n-grams, word n-grams and n-grams of POS tags (in all cases with n varying from 1 to 5). We considered the task of Cross-Topic Authorship Attribution and made experiments on The Guardian corpus. Experimental results show that our method outperforms wordbased embeddings and character n-gram based linear models, which are among the most effective approaches for identifying the writing style of an author. |
