List of Articles natural language processing

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  1 - Adversarial Attacks in a text sentiment Analysis model
  Sahar Mokarrami Sefidab Seyed Abolghasem Mirroshandel hamidreza Ahmadifar Mahdi Mokarrami
  Background and Purpose: Recently some researchers have shown that deep learning models, despite their high accuracy, can be vulnerable through some manipulations of their input samples. This manipulation leads to the production of new samples called Adversarial examples More

  Background and Purpose: Recently some researchers have shown that deep learning models, despite their high accuracy, can be vulnerable through some manipulations of their input samples. This manipulation leads to the production of new samples called Adversarial examples. These samples are very similar to the original ones, so humans cannot differentiate between these samples and the original, and cannot remove them from the dataset before predicting the model and preventing model errors. Various types of research have been done to generate malicious samples and inject them into the model, among which, the production of text samples has its own difficulties due to the discrete nature of the text. In this research, we tried to reach the highest level of vulnerability by providing a method with the least manipulation of the input data, and by testing the proposed method, we were able to bring the accuracy of CNN and LSTM models to less than 10%.Methods: In this research, for making malicious samples, first, a word that can increase the amount of error in the classification prediction is selected from the word dictionary as a candidate word for replacement by using Taylor expansion and then considering the importance of each word in the calculated cost of the corresponding candidate word, we proposed an arrangement for substitution between words. Finally, we moved the words in the specified order until the output of the model changed.Results: The evaluation of the presented method on two sentiment analysis models, LSTM and CNN, has shown that the proposed method has been very effective in reducing the accuracy of both models to less than 10% with a small number of replacements and this indicates the success of the proposed method compared to some other similar methods.Conclusion: As mentioned, most of the attention of science and industry is on the production of different systems using deep learning methods, so their security of them is also important. It is important to increase the strength of the models against adversarial examples. In this research, a method with the least amount of manipulation was presented to produce textual conflict samples. It seems that in the future it will be possible to use different methods of making natural texts to produce samples that, in addition to the apparent similarity to the original sample, are also comprehensible in terms of content. Manuscript profile
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  2 - Intelligent (Language) Tutoring Systems: A Second-order Meta-analytic Review
  Hossein Heidari Tabrizi Mahmoud Jafarie
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  3 - Providing suitable literary alternatives to sentences through text mining
  Behzad Lak Mojgan Tanbakoosaz
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  4 - Deep learning for stock market forecasting using numerical and textual information (Long-Short Term Memory approach)
  seyyedeh mozhgan beheshti masalegou Mohammad ali Afshar kazemi jalal Haghighat monfared Ali Rezaeian
  Stock prices are influenced by many factors, making forecasting challenging. This prediction is often ineffective if it only considers numerical data or textual information. This research aims to provide a method of forecasting the future price of stocks based on the st More

  Stock prices are influenced by many factors, making forecasting challenging. This prediction is often ineffective if it only considers numerical data or textual information. This research aims to provide a method of forecasting the future price of stocks based on the structure of a deep neural network using price data, a set of technical indicators, and news headlines as input to the model. For this purpose, Dow Jones stock data and Reddit channel news data have been used. Technical features are extracted from the stock data, and the news data are converted into a feature vector by the Bag of Words method and fed into the Long-Short term memory network for prediction. Accuracy is used as a performance evaluation measure and experiments on two data sets. The only numerical and only text has been used to evaluate the simultaneous use of two information sources. Also, three networks, SVM, MLP, and RNN, have been used to evaluate the model. The results show that the LSTM model achieved the highest prediction accuracy of 69.19% using news and financial data. News data is 65.62% accurate, and numerical data is 51.89%. Also, the LSTM model performs better than SVM, MLP, and RNN neural networks. Manuscript profile