Lithuanian Author Profiling with the Deep Learning

Kapociute-Dzikicne, Jurgita; Damaševičius, Robertas

doi:10.15439/2018f22

Cited by 4 publications

(3 citation statements)

References 16 publications

(25 reference statements)

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“…The authors of [26] solved the problem aimed to determine the gender and age of the authors of Lithuanian texts. LSTM and CNN applied on the top of Lithuanian neural word embeddings were used.…”

Section: Related Work On Classical Machine Learning Methods and Deep Neural Network For Authorship Attributionmentioning

confidence: 99%

Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection

et al. 2021

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Authorship attribution is one of the important fields of natural language processing (NLP). Its popularity is due to the relevance of implementing solutions for information security, as well as copyright protection, various linguistic studies, in particular, researches of social networks. The article is a continuation of the series of studies aimed at the identification of the Russian-language text’s author and reducing the required text volume. The focus of the study was aimed at the attribution of textual data created as a product of human online activity. The effectiveness of the models was evaluated on the two Russian-language datasets: literary texts and short comments from users of social networks. Classical machine learning (ML) algorithms, popular neural networks (NN) architectures, and their hybrids, including convolutional neural network (CNN), networks with long short-term memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and fastText, that have not been used in previous studies, were applied to solve the problem. A particular experiment was devoted to the selection of informative features using genetic algorithms (GA) and evaluation of the classifier trained on the optimal feature space. Using fastText or a combination of support vector machine (SVM) with GA reduced the time costs by half in comparison with deep NNs with comparable accuracy. The average accuracy for literary texts was 80.4% using SVM combined with GA, 82.3% using deep NNs, and 82.1% using fastText. For social media comments, results were 66.3%, 73.2%, and 68.1%, respectively.

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Section: Related Work On Classical Machine Learning Methods and Deep Neural Network For Authorship Attributionmentioning

confidence: 99%

Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection

et al. 2021

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“…Recently, DL methods have been used. For example, in Kapociute-Dzikiene and Damasevicius, 26 the authors used two DL methods for solving the gender and age identification problem for Lithuanian neural word embeddings. "LSTM" corresponds to the first method and "CNN" to the second one.…”

Section: Related Workmentioning

confidence: 99%

“…When examining closely the concerns of each age group, we find out that young people (13-17 years) speak more about sport, friendship, and the Internet. Unlike teenagers, middle-aged (23)(24)(25)(26)(27) discuss more issues related to money and shopping. Finally, the elderly people (33-47) speak more about medicine and family.…”

Section: Improving Age Prediction Using Svmmentioning

confidence: 99%

A decision system for computational authors profiling: From machine learning to deep learning

Mechti

Krichen

Noureddine

et al. 2020

Concurrency and Computation

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In this study, we tackle the problem of author profiling. The aim of the proposed approach is to determine the author's age and gender. Once the user connects to the company website, this company collects the available data about him (which is usually very limited). Then, the user receives a service recommendation according to his gender and age. Thus, a context-specific decision-making system based on these limited data is required to produce an efficient classification. Such a decision system allows companies to promote their marketing. To obtain the best categorization, machine learning (ML) and deep learning (DL) techniques have been applied in the literature. In this article, we apply both classical ML techniques and recently developed DL techniques. More precisely, we adopt the gated recurrent unit model. Our experiments show that our findings are positively comparable with the best state-of-the-art methods.

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Identification of Age and Gender in Pinterest by Combining Textual and Deep Visual Features

Bravo-Marmolejo

Moreno

Gómez

et al. 2019

Communications in Computer and Information Science

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Lithuanian Author Profiling with the Deep Learning

Cited by 4 publications

References 16 publications

Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection

Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection

A decision system for computational authors profiling: From machine learning to deep learning

Identification of Age and Gender in Pinterest by Combining Textual and Deep Visual Features

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