Deep Feature Generation for Author Identification

Year 2021, , 137 - 143, 28.06.2021

Şükrü Ozan , Davut Emre Taşar , Umut Özdil

https://doi.org/10.18466/cbayarfbe.846016

Abstract

Identifying the authors of a given set of text is a well addressed and complicated task. It requires thorough knowledge of different authors’ writing styles and discriminating them. As the main contribution of this paper, we propose to perform this task using machine learning and deep learning methods, state-of-the-art algorithms, and methods used in numerous complex Natural Language Processing (NLP) problems. We used a text corpus of daily newspaper columns written by thirty authors to perform our experiments. The experimental results proved that document embeddings trained via neural network architecture achieve cutting edge accuracy in learning writing styles and identifying authors of given writings even though the dataset has a considerably unbalanced distribution. We represent our experimental results and outsource our codes for interested readers and natural language processing (NLP) enthusiasts as a GitHub repository. They can reproduce and confirm the results and modify them according to their own needs.

Keywords

Natural Language Processing, Document Embeddings, Logistic Regression, Support Vector Machines, Author Identification

Supporting Institution

TÜBİTAK

Project Number

3190585

Thanks

This work is a part of the project supported by the Scientific and Technological Research Council of Turkey (TUBITAK) TEYDEB-1501 program under Project no 3190585, and named “General Purpose Chatbot Application That Can Produce Meaningful Dialog via Machine Learning Algorithms”.

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