Author Verification Using a Semantic Space Model

Ángel Hernández-Castañeda, Hiram Calvo


In this work we propose to solve the author verification problem using a semantic space model through Latent Dirichlet Allocation (LDA). We experiment with the corpus used in the author identification tasks at PAN 2014 and PAN 2015. These datasets consist of subsets in the following languages: English, Spanish, Dutch and Greek. Each problem contained in these corpora is formed by one to five known documents which were written by one author and one unknown document. The task is to predict whether the unknown document was written by the author who wrote the known documents. We processed the documents in the dataset and captured the fingerprint of authors by generating a probabilistic distribution of words in the documents. In PAN 2015 classification, we achieved 81.6%, 75.4%, 74.1%, 67.1% accuracy for each English, Spanish, Dutch and Greek subset respectively. In particular for the English subset, we outreached the best result reported in both  competitions.


Author verification, semantic space model, cross-genre, cross-topic, latent Dirichlet allocation

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