Deep Learning Based vs. Markov Chain Based Text Generation for Cross Domain Adaptation for Sentiment Classification

Abdelwahab, Omar; Elmaghraby, Adel

doi:10.1109/iri.2018.00046

Cited by 17 publications

(5 citation statements)

References 4 publications

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“…Their findings suggest that synthetic data can yield better results than poorer quality original data. Similarly, Abdelwahab and Elmaghraby [22] compared LSTM, RNN, gated recurrent unit (GRU), and Markov chain models, concluding that deep learning models handle incorrectly labeled reviews better than Markov chain-based text generators. A shift towards structuring data was observed with Marcheggiani and Perez-Beltrachini [23] using graph convolutional networks on the web natural language generator (WebNLG) and SR11Deep datasets, demonstrating the benefits of encoding structural information with s graph convolutional networks (GCN).…”

Section: Related Workmentioning

confidence: 99%

A multimodal machine learning approach to generate news articles from geo-tagged images

Gotmare,

Thite,

Bewoor

2024

IJECE

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Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by leveraging cutting-edge developments in machine learning, image captioning, and advanced text generation technologies. Employing a multimodal approach that integrates machine learning and transformer algorithms, such as visual geometry group network16 (VGGNet16), convolutional neural network (CNN) and a long short-term memory (LSTM) based system, the algorithm initiates by extracting the location from exchangeable image file format (Exif) data from the image. The features are extracted from the image and corresponding news headline is generated. The headlines are used for generating a comprehensive article with contemporary large language model (LLM). Further, the algorithm generates the news article big-science large open-science open-access multilingual language model (BLOOM). The algorithm was tested on real time photographs as well as images from the internet. In both the cases the news articles generated were validated with ROUGE and BULE score. The proposed work is found to be successful attempt in journalism field.

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Section: Related Workmentioning

confidence: 99%

A multimodal machine learning approach to generate news articles from geo-tagged images

Gotmare,

Thite,

Bewoor

2024

IJECE

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“…To speed up the process and improve the accuracy deep learning algorithm can be used for classification. Many studies [16][17][18][19][20][21][22][23][24][25][26][27][28] have shown that the use of deep learning can increase the accuracy of classification on NLP. From the previous studies, it can be concluded that modifications of CNN and RNN's enhancement most results the higher accuracy than others.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Complaint Classification for Indonesia Telecommunication Company's Call Center

Lukitasari¹,

Hidayat²

2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

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The preliminary research was held to utilize the call center conversations records from a broadband telecommunications company in Indonesia. There is a need from the company to classify customer's complaints automatically by a system to minimize human errors and at once streamline the business processes and resources. Natural Language Processing (NLP), as an integral part of artificial intelligence (AI), empower machines to understand human languages for performing beneficial tasks. The growth of deep learning is the main driver behind NLP for performing various practical applications and business, therefore deep learning is expected to overcome the problems encountered. This paper explains the methods used in designing the classification systems based on deep learning. A literature review is conducted to find the proper algorithm used in classifying problems. Furthermore, it also explains the stages performed in preparing the data and building the system model. From experiments conducted, it can be stated that the RNN algorithm can be used in the classification of customer complaints with the results shown by the accuracy value of the model.

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“…2) Sequence-Vector Model -Input is of variable size, and output is a fixed-size vector. Classification is an example of this model [4]. 3) Sequence-to-Sequence Model -Input and output are variable sizes in this model type.…”

Section: Introductionmentioning

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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Deep Learning Based vs. Markov Chain Based Text Generation for Cross Domain Adaptation for Sentiment Classification

Cited by 17 publications

References 4 publications

A multimodal machine learning approach to generate news articles from geo-tagged images

A multimodal machine learning approach to generate news articles from geo-tagged images

Deep Learning-Based Complaint Classification for Indonesia Telecommunication Company's Call Center

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

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