Multi-classifier system for authorship verification task using word embeddings

Document Type

Conference Proceeding

Source of Publication

2nd International Conference on Natural Language and Speech Processing, ICNLSP 2018

Publication Date

6-6-2018

Abstract

© 2018 IEEE. Authorship Verification is considered as a topic of growing interest in research, which has shown excellent development in recent years. We want to know if an unknown document belongs to the documents set known to an author or not. Classical text classifiers often focus on many human designed features, such as dictionaries, knowledge bases and special tree kernels. Other studies use the N-gram function that often leads to the curse of dimensionality. Contrary to traditional approaches, this article proposes a new scheme of Machine Learning model based on fusion of three different architectures namely, Convolutional Neural Networks, Recurrent-Convolutional Neural Networks and Support Vector Machine classifiers without human-designed features. Word2vec based Word Embeddings is proposed to learn the best word representations for automatic authorship verification. Word Embeddings provides semantic vectors and extracts the most relevant information about raw text with a relatively small dimension. As well as the classifiers generally make different errors on the same learning samples which results in a combination of several points of view to maintain relevant information contained in different classifiers. The final decision of our system is obtained by combining the results of the three models using the voting method.

ISBN

9781538645437

Publisher

Institute of Electrical and Electronics Engineers Inc.

First Page

1

Last Page

6

Disciplines

Computer Sciences

Keywords

Authorship Verification, Convolutional Neural Networks (CNN), Deep Learning, Natural Language Processing (NLP), Recurrent-Convolutional Neural Networks R-CNN, Word Embeddings

Scopus ID

85049361840

Indexed in Scopus

yes

Open Access

no

Link to Full Text

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