Author Identification in Literature Texts Through Deep Learning

Student: Jin Seungmin

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2018

The main problem of previous models is hard to scale out and to update when new big data comes. This paper suggests a novel architecture to parallelize previous monolithic deep learning models in author identification task. On the top of the parallelized deep learning models, we build a special layer which reducing searching spaces called Skim-Trigger. It outperforms previous models in terms of training and execution speed.

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