On domain independence of author identification

Authors:
Masato Shirai;Takao Miura
Affiliations:
HOSEI University, Dept.of Elect.& Elect. Engr., Koganei, Tokyo, Japan;HOSEI University, Dept.of Elect.& Elect. Engr., Koganei, Tokyo, Japan
Venue:
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Year:
2011

Citing 3
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Abstract

Latent Dirichlet Allocation (LDA) is a probabilistic framework by which we may assume each word carries probability distribution to each topic and a topic carries a distribution to each document. By putting all the documents together into one collection by each author, it is possible to identify authors. Here we show that author identification is fully reliable within a framework of LDA independent of documents domains by learning incomplete and massive documents.