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Communication Dans Un Congrès Année : 2018

Author Identification Using Latent Dirichlet Allocation

Résumé

We tackle the task of author identification at PAN 2015 through a Latent Dirichlet Allocation (LDA) model. By using this method, we take into account the vocabulary and context of words at the same time, and after a statistical process find to what extent the relations between words are given in each document; processing a set of documents by LDA returns a set of distributions of topics. Each distribution can be seen as a vector of features and a fingerprint of each document within the collection. We used then a Naïve Bayes classifier on the obtained patterns with different performances. We obtained state-of-the-art performance for English, overtaking the best FS score reported in PAN 2015, while obtaining mixed results for other languages.
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Dates et versions

hal-03907314 , version 1 (20-12-2022)

Identifiants

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Hiram Calvo, Ángel Hernández-Castañeda, Jorge Garcia Flores. Author Identification Using Latent Dirichlet Allocation. 18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017, Apr 2017, Budapest (Hungary)., Hungary. pp.303-312, ⟨10.1007/978-3-319-77116-8_22⟩. ⟨hal-03907314⟩
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