scholarly journals Evaluation of the Coherence of Polish Texts Using Neural Network Models

2021 ◽  
Vol 11 (7) ◽  
pp. 3210
Author(s):  
Sergii Telenyk ◽  
Sergiy Pogorilyy ◽  
Artem Kramov
Keyword(s):  
Neural Networks ◽  
Language Processing ◽  
Word Order ◽  
State Of The Art ◽  
Network Models ◽  
Neural Network Models ◽  
Similarity Graph ◽  
Coherence Estimation ◽  
The Impact ◽  
Machine Learning Models

Coherence evaluation of texts falls into a category of natural language processing tasks. The evaluation of texts’ coherence implies the estimation of their semantic and logical integrity; such a feature of a text can be utilized during the solving of multidisciplinary tasks (SEO analysis, medicine area, detection of fake texts, etc.). In this paper, different state-of-the-art coherence evaluation methods based on machine learning models have been analyzed. The investigation of the effectiveness of different methods for the coherence estimation of Polish texts has been performed. The impact of text’s features on the output coherence value has been analyzed using different approaches of a semantic similarity graph. Two neural networks based on LSTM layers and a pre-trained BERT model correspondingly have been designed and trained for the coherence estimation of input texts. The results obtained may indicate that both lexical and semantic components should be taken into account during the coherence evaluation of Polish documents; moreover, it is advisable to analyze corresponding documents in a sentence-by-sentence manner taking into account word order. According to the retrieved accuracy of the proposed neural networks, it can be concluded that suggested models may be used in order to solve typical coherence estimation tasks for a Polish corpus.

scholarly journals Arabic text summarization using deep learning approach

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Molham Al-Maleh ◽  
Said Desouki
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Language Processing ◽  
Network Models ◽  
Arabic Language ◽  
Text Summarization ◽  
Neural Network Models ◽  
Data Set ◽  
Learning Techniques ◽  
Arabic Text Summarization

AbstractNatural language processing has witnessed remarkable progress with the advent of deep learning techniques. Text summarization, along other tasks like text translation and sentiment analysis, used deep neural network models to enhance results. The new methods of text summarization are subject to a sequence-to-sequence framework of encoder–decoder model, which is composed of neural networks trained jointly on both input and output. Deep neural networks take advantage of big datasets to improve their results. These networks are supported by the attention mechanism, which can deal with long texts more efficiently by identifying focus points in the text. They are also supported by the copy mechanism that allows the model to copy words from the source to the summary directly. In this research, we are re-implementing the basic summarization model that applies the sequence-to-sequence framework on the Arabic language, which has not witnessed the employment of this model in the text summarization before. Initially, we build an Arabic data set of summarized article headlines. This data set consists of approximately 300 thousand entries, each consisting of an article introduction and the headline corresponding to this introduction. We then apply baseline summarization models to the previous data set and compare the results using the ROUGE scale.

Recurrent convolutional neural networks for poet identification

2020 ◽  
Author(s):  
Dariush Salami ◽  
Saeedeh Momtazi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Short Term Memory ◽  
State Of The Art ◽  
The State ◽  
Support Vector ◽  
Neural Network Models ◽  
Spatial Features ◽  
Input Text

Abstract Deep neural networks have been widely used in various language processing tasks. Recurrent neural networks (RNNs) and convolutional neural networks (CNN) are two common types of neural networks that have a successful history in capturing temporal and spatial features of texts. By using RNN, we can encode input text to a lower space of semantic features while considering the sequential behavior of words. By using CNN, we can transfer the representation of input text to a flat structure to be used for classifying text. In this article, we proposed a novel recurrent CNN model to capture not only the temporal but also the spatial features of the input poem/verse to be used for poet identification. Considering the shortcomings of the normal RNNs, we try both long short-term memory and gated recurrent unit units in the proposed architecture and apply them to the poet identification task. There are a large number of poems in the history of literature whose poets are unknown. Considering the importance of the task in the information processing field, a great variety of methods from traditional learning models, such as support vector machine and logistic regression, to deep neural network models, such as CNN, have been proposed to address this problem. Our experiments show that the proposed model significantly outperforms the state-of-the-art models for poet identification by receiving either a poem or a single verse as input. In comparison to the state-of-the-art CNN model, we achieved 9% and 4% improvements in f-measure for poem- and verse-based tasks, respectively.

scholarly journals On the Impact of Interpretability Methods in Active Image Augmentation Method

2021 ◽  
Author(s):  
Flávio Arthur Oliveira Santos ◽  
Cleber Zanchettin ◽  
Leonardo Nogueira Matos ◽  
Paulo Novais
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Network Models ◽  
Training Data ◽  
Learning Models ◽  
Neural Network Models ◽  
Wide Range ◽  
The Impact ◽  
Activation Mapping ◽  
Machine Learning Models

Abstract Robustness is a significant constraint in machine learning models. The performance of the algorithms must not deteriorate when training and testing with slightly different data. Deep neural network models achieve awe-inspiring results in a wide range of applications of computer vision. Still, in the presence of noise or region occlusion, some models exhibit inaccurate performance even with data handled in training. Besides, some experiments suggest deep learning models sometimes use incorrect parts of the input information to perform inference. Active image augmentation (ADA) is an augmentation method that uses interpretability methods to augment the training data and improve its robustness to face the described problems. Although ADA presented interesting results, its original version only used the vanilla backpropagation interpretability to train the U-Net model. In this work, we propose an extensive experimental analysis of the interpretability method’s impact on ADA. We use five interpretability methods: vanilla backpropagation, guided backpropagation, gradient-weighted class activation mapping (GradCam), guided GradCam and InputXGradient. The results show that all methods achieve similar performance at the ending of training, but when combining ADA with GradCam, the U-Net model presented an impressive fast convergence.

NEURAL NETWORK MODELS TO PREDICT PENETRATION OF WILD BOAR INTO CULTIVATED FIELDS

1996 ◽  
Vol 04 (03) ◽  
pp. 433-444 ◽  
Author(s):  
F. SPITZ ◽  
S. LEK ◽  
I. DIMOPOULOS
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Backpropagation Algorithm ◽  
Neural Network Models ◽  
Ecological Problems ◽  
Need To Evaluate ◽  
Good Determination ◽  
The Impact

Wildlife managers need to evaluate the regional risk of damage by big game in any cultivated plot. Nevertheless, such an evaluation can be biased by nonlinearity, a common difficulty when facing ecological problems. We propose a model for the impact of wild boars on cultivated fields, based on artificial neural networks, with a backpropagation algorithm. The first model, predicting the frequency of impact on a particular plot, gives a good determination coefficient (R2=0.91). The second model, predicting the presence or absence of impact during a particular week, gives over 80 % correct results.

scholarly journals The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks

2020 ◽  
Vol 6 (53) ◽  
pp. 286-303
Author(s):  
Yervand Nahapetyan
Keyword(s):  
Neural Networks ◽  
Negative Impact ◽  
Moving Average ◽  
Network Models ◽  
Real Data ◽  
Short Term ◽  
Neural Network Models ◽  
The Difference ◽  
The Impact ◽  
The Revolution

AbstractThis article primarily aims to estimate the impact of the Armenian revolution and test the hypothesis, that is, the benefits of revolution and establishment of democracy can be seen even in the first year after the political change. To calculate the short-term net surplus of the revolution, we estimated the difference between the projection of Armenian economic activity for the four quarters after the revolution, using only pre-revolutionary (assuming there was no revolution) and real data for the same period after the revolution. Using deep neural network models, such as recurrent neural networks and convolutional neural networks (CNN), we compared prediction accuracy with structural econometrics, such as autoregressive integrated moving average and error correction model, using pre-revolutionary data (2000Q1–2018Q1) for Armenia and combinations of models using an ensembling mechanism. As a result, CNN overperformed the rest of the models. The CNN simulation on post-revolutionary data indicates that during the period 2018-Q2–2019-Q1, Armenia gained approximately 850 million EUR in terms of GDP, thanks to the revolution and the new government. Moreover, out of seven models, the five best models in terms of accuracy indicated that the revolution had no negative impact on the Armenian economy, as the actual values were within or above the 95% confidence interval of the prediction.

Machine Learning Crowdfunding

2020 ◽  
Vol 10 (2) ◽  
pp. 1-11
Author(s):  
Evangelos Katsamakas ◽  
Hao Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Language Processing ◽  
Topic Modeling ◽  
Network Models ◽  
Unstructured Data ◽  
Text Analytics ◽  
Learning Models ◽  
Neural Network Models ◽  
Machine Learning Models

Crowdfunding is a novel and important economic mechanism for funding projects and promoting innovation in the digital economy. This article explores most recent structured and unstructured data from a crowdfunding platform. It provides an in-depth exploration of the data using text analytics techniques, such as sentiment analysis and topic modeling. It uses novel natural language processing to represent project descriptions, and evaluates machine learning models, including neural network models, to predict project fundraising success. It discusses the findings of the performance evaluation, and summarizes lessons for crowdfunding platforms and their users.

scholarly journals Opening the black box of neural networks: methods for interpreting neural network models in clinical applications

2018 ◽  
Vol 6 (11) ◽  
pp. 216-216 ◽  
Author(s):  
Zhongheng Zhang ◽  
◽  
Marcus W. Beck ◽  
David A. Winkler ◽  
Bin Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Black Box ◽  
Clinical Applications ◽  
Neural Network Models

scholarly journals Analysis Methods in Neural Language Processing: A Survey

2019 ◽  
Vol 7 ◽  
pp. 49-72 ◽  
Author(s):  
Yonatan Belinkov ◽  
James Glass
Keyword(s):  
Language Processing ◽  
Network Models ◽  
Research Trends ◽  
Neural Network Models ◽  
Fine Grained ◽  
Survey Paper ◽  
Review Analysis ◽  
Analysis Methods ◽  
New Models ◽  
Future Work

The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work.

Predicting the exchange rate in Sudan using neural networks models during the period (1960 - 2017): التنبؤ بسعر الصرف في السودان باستخدام نماذج الشبكات العصبية خلال الفترة (1960م – 2017م)

2020 ◽  
Vol 4 (14) ◽  
Author(s):  
Fathi Ahmed Ali Adam, Mahmoud Mohamed Abdel Aziz Gamal El-Di
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Network Models ◽  
Neural Network Models ◽  
Classical Models ◽  
Artificial Neural ◽  
The Exchange Rate ◽  
Long Term Forecasting ◽  
Artificial Neural Network Models

The study examined the use of artificial neural network models to predict the exchange rate in Sudan through annual exchange rate data between the US dollar and the Sudanese pound. This study aimed to formulate the models of artificial neural networks in which the exchange rate can be predicted in the coming period. The importance of the study is that it is necessary to use modern models to predict instead of other classical models. The study hypothesized that the models of artificial neural networks have a high ability to predict the exchange rate. Use models of artificial neural networks. The most important results ability of artificial neural networks models to predict the exchange rate accurately, Form MLP (1-1-1) is the best model chosen for that purpose. The study recommended the development of the proposed model for long-term forecasting.

scholarly journals Robotic grasp detection using a novel two-stage approach

2021 ◽  
Vol 1 (1) ◽  
pp. 19-29
Author(s):  
Zhe Chu ◽  
Mengkai Hu ◽  
Xiangyu Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Particle Swarm Optimizer ◽  
Neural Network Models ◽  
Two Stage ◽  
The Neural Network ◽  
End To End ◽  
Small Change ◽  
Robotic Grasp

Recently, deep learning has been successfully applied to robotic grasp detection. Based on convolutional neural networks (CNNs), there have been lots of end-to-end detection approaches. But end-to-end approaches have strict requirements for the dataset used for training the neural network models and it’s hard to achieve in practical use. Therefore, we proposed a two-stage approach using particle swarm optimizer (PSO) candidate estimator and CNN to detect the most likely grasp. Our approach achieved an accuracy of 92.8% on the Cornell Grasp Dataset, which leaped into the front ranks of the existing approaches and is able to run at real-time speeds. After a small change of the approach, we can predict multiple grasps per object in the meantime so that an object can be grasped in a variety of ways.

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