Automatic Text Generation in Macedonian Using Recurrent Neural Networks

Milanova, Ivona; Sarvanoska, Ksenija; Srbinoski, Viktor; Gjoreski, Hristijan

doi:10.1007/978-3-030-33110-8_1

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…The models achieved optimum results with 100 epochs and a batch size of 32 after testing various combinations of hyperparameters. The model was compiled with the Adam optimiser [ 53 ] and sparse categorical cross-entropy loss function [ 54 ]. All three classifiers included 80% data for training and 20% for testing the models.…”

Section: Methodsmentioning

confidence: 99%

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

Adak

Pradhan

Shukla

et al. 2022

Foods

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The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs such as Uber Eats, Menulog, and Deliveroo, customer reviews on internet platforms have become a valuable source of information about a company’s performance. FDS organisations strive to collect customer complaints and effectively utilise the information to identify improvements needed to enhance customer satisfaction. However, only a few customer opinions are addressed because of the large amount of customer feedback data and lack of customer service consultants. Organisations can use artificial intelligence (AI) instead of relying on customer service experts and find solutions on their own to save money as opposed to reading each review. Based on the literature, deep learning (DL) methods have shown remarkable results in obtaining better accuracy when working with large datasets in other domains, but lack explainability in their model. Rapid research on explainable AI (XAI) to explain predictions made by opaque models looks promising but remains to be explored in the FDS domain. This study conducted a sentiment analysis by comparing simple and hybrid DL techniques (LSTM, Bi-LSTM, Bi-GRU-LSTM-CNN) in the FDS domain and explained the predictions using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The DL models were trained and tested on the customer review dataset extracted from the ProductReview website. Results showed that the LSTM, Bi-LSTM and Bi-GRU-LSTM-CNN models achieved an accuracy of 96.07%, 95.85% and 96.33%, respectively. The model should exhibit fewer false negatives because FDS organisations aim to identify and address each and every customer complaint. The LSTM model was chosen over the other two DL models, Bi-LSTM and Bi-GRU-LSTM-CNN, due to its lower rate of false negatives. XAI techniques, such as SHAP and LIME, revealed the feature contribution of the words used towards positive and negative sentiments, which were used to validate the model.

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Section: Methodsmentioning

confidence: 99%

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

Adak

Pradhan

Shukla

et al. 2022

Foods

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show abstract

“…The loss function computes the negative log probability of the true class, where it equals zero if the predicted probability of the true class is one, and as the predicted probability approaches zero, the loss function approaches infinity [60]. The sparse categorical cross-entropy loss function from the Tensorflow library [56] was used, which calculates the cross-entropy loss between the labels and predictions [56,61].…”

Section: Categorical Cross-entropy Loss Functionmentioning

confidence: 99%

A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification

Dastour

Hassan

2023

Sustainability

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The pace of Land Use/Land Cover (LULC) change has accelerated due to population growth, industrialization, and economic development. To understand and analyze this transformation, it is essential to examine changes in LULC meticulously. LULC classification is a fundamental and complex task that plays a significant role in farming decision making and urban planning for long-term development in the earth observation system. Recent advances in deep learning, transfer learning, and remote sensing technology have simplified the LULC classification problem. Deep transfer learning is particularly useful for addressing the issue of insufficient training data because it reduces the need for equally distributed data. In this study, thirty-nine deep transfer learning models were systematically evaluated alongside multiple deep transfer learning models for LULC classification using a consistent set of criteria. Our experiments will be conducted under controlled conditions to provide valuable insights for future research on LULC classification using deep transfer learning models. Among our models, ResNet50, EfficientNetV2B0, and ResNet152 were the top performers in terms of kappa and accuracy scores. ResNet152 required three times longer training time than EfficientNetV2B0 on our test computer, while ResNet50 took roughly twice as long. ResNet50 achieved an overall f1-score of 0.967 on the test set, with the Highway class having the lowest score and the Sea Lake class having the highest.

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“…Papers Human-centric (HC) [50], [53], [100] Machine-centric (MC) [60], [63]- [67], [83], [84], [86] [3], [69]- [71], [74], [95], [99], [122] [72], [73], [75]- [80] [21], [82], [90]- [92], [94], [102], [125] [6], [48], [49], [51], [52], [55], [56], [126] [18], [57], [58], [115], [119], [127], [128] [104], [112]- [114], [120], [129] [105], [106], [108], [110], [116], [117], [123] Both [5], [54], [59],…”

Section: Metrics Groupmentioning

confidence: 99%

A Systematic Literature Review on Text Generation Using Deep Neural Network Models

et al. 2022

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In recent years, significant progress has been made in text generation. The latest text generation models are revolutionizing the domain by generating human-like text. It has gained wide popularity recently in many domains like news, social networks, movie script writing, poetry composition, to name a few. The application of text generation in various fields has resulted in a lot of interest from the scientific community in this area. To the best of our knowledge, there is a lack of extensive review and up-to-date body of knowledge of text generation deep learning models. Therefore, this paper aims to bring together all the relevant work in a systematic mapping study highlighting key contributions from various researchers over the years, focusing on the past, present, and future trends. In this work, we have identified 90 primary studies from 2015 to 2021 employing the PRISMA framework. We also identified research gaps that are further needed to be explored by the research community. In the end, we provide some future directions for researchers and guidelines for practitioners based on the findings of this review.

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Automatic Text Generation in Macedonian Using Recurrent Neural Networks

Cited by 6 publications

References 13 publications

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification

A Systematic Literature Review on Text Generation Using Deep Neural Network Models

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