Author Identification Using Latent Dirichlet Allocation

被引：0

作者：

Calvo, Hiram ^{[1
,2
]}

Hernandez-Castaneda, Angel ^{[1
]}

Garcia-Flores, Jorge ^{[2
]}

机构：

[1] IPN, Ctr Comp Res CIC, Ave JD Batiz E MO Mendizabal, Mexico City 07738, DF, Mexico

[2] Univ Paris 13, Lab Informat Paris Nord, CNRS, UMR 7030,Sorbonne Paris Cite, F-93430 Villetaneuse, France

来源：

COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2017, PT II | 2018年 / 10762卷

关键词：

D O I：

10.1007/978-3-319-77116-8_22

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 Naive 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.

引用

页码：303 / 312

页数：10

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