Author Identification on Literature in Different Languages: A Systematic Survey

Digamberrao, Kale Sunil; Prasad, Jayashree R.

doi:10.1109/icacct.2018.8529635

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Several approaches have been experimented with to solve different authorship-related problems. In this way, the authors (Digamberrao & Prasad, 2018) have presented various techniques used in author identification in different languages. They concluded that no one approach is used exclusively for author identification; rather, researchers apply a variety of techniques depending on the characteristics of the understudied language, the training data set, and the feature set.…”

Section: Authorship Detection Techniquesmentioning

confidence: 99%

A Survey on Authorship Analysis Tasks and Techniques

2022

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Authorship Analysis (AA) is a natural language processing field that examines the previous works of writers to identify the author of a text based on its features. Studies in authorship analysis include authorship identification, authorship profiling, and authorship verification. Due to its relevance, to many applications in this field attention has been paid. It is widely used in the attribution of historical literature. Other applications include legal linguistics, criminal law, forensic investigations, and computer forensics. This paper aims to provide an overview of the work done and the techniques applied in the authorship analysis domain. The examination of recent developments in this field is the principal focus. Many different criteria can be used to define a writer’s style. This paper investigates stylometric features in different author-related tasks, including lexical, syntactic, semantic, structural, and content-specific ones. A lot of classification methods have been applied to authorship analysis tasks. We examine many research studies that use different machine learning and deep learning techniques. As a means of pointing the direction for future studies, we present the most relevant methods recently proposed. The reviewed studies include documents of different types and different languages. In summary, due to the fact that each natural language has its own set of features, there is no standard technique generically applicable for solving the AA problem.

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Section: Authorship Detection Techniquesmentioning

confidence: 99%

A Survey on Authorship Analysis Tasks and Techniques

2022

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“…Therefore, Zhang et al [20] introduced a discriminator to filter sentences in the translation model to enhance the quality of the augmented data. Moreover, Digamberrao et al [21] presented language translation issues and effects on different languages. In addition, Perevalov et al [22] and Bornea et al [23] employed different languages corpus to construct a multilingual translation model, and the model could improve the accuracy of the augmented data.…”

Section: Related Workmentioning

confidence: 99%

MDA: An Intelligent Medical Data Augmentation Scheme Based on Medical Knowledge Graph for Chinese Medical Tasks

Shi

Zhang²,

Huang³

et al. 2022

Applied Sciences

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Text data augmentation is essential in the field of medicine for the tasks of natural language processing (NLP). However, most of the traditional text data augmentation focuses on the English datasets, and there is little research on the Chinese datasets to augment Chinese sentences. Nevertheless, the traditional text data augmentation ignores the semantics between words in sentences, besides, it has limitations in alleviating the problem of the diversity of augmented sentences. In this paper, a novel medical data augmentation (MDA) is proposed for NLP tasks, which combines the medical knowledge graph with text data augmentation to generate augmented data. Experiments on the named entity recognition task and relational classification task demonstrate that the MDA can significantly enhance the efficiency of the deep learning models compared to cases without augmentation.

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Author Identification using Sequential Minimal Optimization with rule-based Decision Tree on Indian Literature in Marathi

Digamberrao

Prasad²

2018

Procedia Computer Science

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Author Identification on Literature in Different Languages: A Systematic Survey

Cited by 5 publications

References 23 publications

A Survey on Authorship Analysis Tasks and Techniques

A Survey on Authorship Analysis Tasks and Techniques

MDA: An Intelligent Medical Data Augmentation Scheme Based on Medical Knowledge Graph for Chinese Medical Tasks

Author Identification using Sequential Minimal Optimization with rule-based Decision Tree on Indian Literature in Marathi

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