A Comprehensive Study of Learning Approaches for Author Gender Identification
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Dosyalar
Tarih
2022-09-23
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Kauno Technologijos Universitetas
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Abstract: In recent years, author gender identification is an important yet challenging task in the fields of information retrieval and computational linguistics. In this paper, different learning approaches are presented to address the problem of author gender identification for Turkish articles. First, several classification algorithms are applied to the list of representations based on different paradigms: fixed-length vector representations such as Stylometric Features (SF), Bag-of-Words (BoW) and distributed word/document embeddings such as Word-2vec, fastText and Doc2vec. Secondly, deep learning architectures, Convolution Neural Network (CNN), Recurrent Neural Network (RNN), special kinds of RNN such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), C-RNN, Bidirectional LSTM (bi-LSTM), Bidirectional GRU (bi-GRU), Hierarchical Attention Networks and Multi-head Attention (MHA) are designated and their comparable performances are evaluated. We conducted a variety of experiments and achieved outstanding empirical results. To conclude, ML algorithms with BoW have promising results. fast-Text is also probably suitable between embedding models. This comprehensive study contributes to literature utilizing different learning approaches based on several ways of representations. It is also first attempt to identify author gender applying SF on Turkish language. © 2022, Kauno Technologijos Universitetas. All rights reserved.
Açıklama
Anahtar Kelimeler
Author gender identification, Deep learning, Embeddings, Stylometric features
Kaynak
Information Technology and Control
WoS Q Değeri
N/A
