Deep Learning neural nets versus traditional machine learning in gender identification of authors of RusProfiling texts

Sboev, Alexander; Moloshnikov, Ivan; Gudovskikh, Dmitry; Selivanov, Anton; Rybka, Roman; Litvinova, Tatiana

doi:10.1016/j.procs.2018.01.065

Cited by 14 publications

(8 citation statements)

References 3 publications

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“…The end-to-end model is used by these approaches to learn and extract the feature, which thereafter executes the classification. This technique has been able to outperform conventional Machine Learning and achieve state-of-theart result performance [11].…”

Section: B Nlp and ML Methodsmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI) towards Model Personality in NLP task

Adi

Nurdin

2021

IJOE

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In recent years, the development of Deep Learning in the field of Natural Language Processing, especially in sentiment analysis, has achieved significant progress and success. It is because of the availability of large amounts of text data and the ability of deep learning techniques to produce sophisticated predictive results from various data features. However, the sophisticated predictions that are not accompanied by sufficient information on what is happening in the model will be a major setback. Therefore, the significant development of the Deep Learning model must be accompanied by the development of the XAI method, which helps provide information about what drives the model to get predictable results. Simple Bidirectional LSTM and complex Bi-GRU-LSTM-CNN model for Sentiment Analysis were proposed in the present research. Both models were analyzed further using three different XAI methods (LIME, SHAP, and Anchor) in which they were used and compared to two proposed models, proving that XAI is not limited to giving information about what happens in the model but can also help us to understand and distinguish models' personality and behaviour. Keywords⎯ Deep learning, Explainable artificial intelligence, Natural language processing, Sentiment analysis I.

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Section: B Nlp and ML Methodsmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI) towards Model Personality in NLP task

Adi

Nurdin

2021

IJOE

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“…DNNs work well when there is a large body of training data and available computational power. DNNs have consistently yielded strong results for a variety of datasets and competitions, such as winning the Large Scale Visual Recognition Challenge (Russakovsky et al 2015) and achieving strong results for energy demand prediction (Paterakis et al 2017), identifying gender of a text author (Sboev et al 2018), stroke prediction (Hung et al 2017), network intrusion detection (Yin et al 2017), speech emotion recognition (Fayek et al 2017), and taxi destination prediction (de Brébisson et al 2015). Since there are many applications which lack large amounts of training data or for which the interpretability of a learned model is important, there is a need to integrate the benefits of DNNs with other classifier algorithms.…”

Section: Conclusion and Directions For Ongoing Researchmentioning

confidence: 99%

A survey of deep network techniques all classifiers can adopt

Ghods

Cook

2020

Data Min Knowl Disc

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Deep neural networks (DNNs) have introduced novel and useful tools to the machine learning community. Other types of classifiers can potentially make use of these tools as well to improve their performance and generality. This paper reviews the current state of the art for deep learning classifier technologies that are being used outside of deep neural networks. Non-neural network classifiers can employ many components found in DNN architectures. In this paper, we review the feature learning, optimization, and regularization methods that form a core of deep network technologies. We then survey non-neural network learning algorithms that make innovative use of these methods to improve classification performance. Because many opportunities and challenges still exist, we discuss directions that can be pursued to expand the area of deep learning for a variety of classification algorithms.

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“…• RusProfiling is a popular dataset for author profiling, including gender identification. Current state of the art results are achieved by Sboev et al (2018).…”

Section: • Rusidiolect 3 Is a Dataset For Experimental Studies Of Thementioning

confidence: 99%

Deep Learning for the Russian Language

Artemova

2020

The Palgrave Handbook of Digital Russia Studies

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Deep learning is a term used to describe artificial intelligence (AI) technologies. AI deals with how computers can be used to solve complex problems in the same way that humans do. Such technologies as computer vision (CV) and natural language processing (NLP) are distinguished as the largest AI areas. To imitate human vision and the ability to express meaning and feelings through language, deep learning exploits artificial neural networks that are trained on real life evidence.While most vision-related tasks are solved using common methods nearly irrespective of target domains, NLP methods strongly depend on the properties of a given language. Linguistic diversity complicates deep learning for NLP. This chapter focuses on deep learning applications to processing the Russian language.

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Deep Learning neural nets versus traditional machine learning in gender identification of authors of RusProfiling texts

Cited by 14 publications

References 3 publications

Explainable Artificial Intelligence (XAI) towards Model Personality in NLP task

Explainable Artificial Intelligence (XAI) towards Model Personality in NLP task

A survey of deep network techniques all classifiers can adopt

Deep Learning for the Russian Language

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