Efficient processing of GRU based on word embedding for text classification

Zulqarnain, Muhammad; Ghazali, Rozaida; Ghouse, Muhammad Ghulam; Mushtaq, Muhammad Faheem

doi:10.30630/joiv.3.4.289

Cited by 61 publications

(38 citation statements)

References 20 publications

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“…A very effective way of handling the embedding vectors using GRU states is a plus for many Natural language processing (NLP) problems such as text classification. The effec-tiveness of GRU is proved with the help of an experimental study conducted by the [27] researchers. They used TREC with 6 distinct classes and Google snippet with 8 distinct classes dataset for proving the strength of GRU with the NLP process for problem-solving.…”

Section: B Deep Learning Techniquesmentioning

confidence: 99%

SeFACED: Semantic-Based Forensic Analysis and Classification of E-Mail Data Using Deep Learning

et al. 2021

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Artificial Intelligence (AI), in combination with the Internet of Things (IoT), called (AIoT), an emerging trend in industrial applications, is capable of intelligent decision-making with self-driven analytic. With its extensive usage in diverse scenarios, IoT devices generate bulk data that gets contrived by attackers to disrupt normal operations and services. Hence, there is a daring need for proactive data analyses that must prevent cyber-attacks and crimes. To investigate crimes involving Electronic Mail (email), analysis of both the header and the email body is required since the semantics of communication helps to identify the source of potential evidence. With the continued growth of data shared via emails, investigators now face the daunting challenge of extracting the required semantic information from the bulks of emails, thereby causing a delay in the investigation process. This gives an edge to the criminal in erasing their footprints of malicious acts. The existing keyword-based search techniques and filtration often result in extraneous, short sequence emails, which skips meaningful information. To overcome the above limitation, we successfully designed a novel efficient approach called SeFACED that uses Long short-term memory (LSTM) based Gated Recurrent Neural Network (GRU) for multiclass email classification. SeFACED not only caters to short sequences but long dependencies of 1000+ characters as well. SeFACED focuses on tuning LSTM based GRU parameters to attain the best performance, which has its assessment by comparing it with traditional Machine Learning (ML) and Deep Learning (DL) models and state-of-the-art studies on the subject. Experimental results on self-extended benchmark datasets exhibit that SeFACED effectively outperforms existing methods while keeping the classification process robust and reliable.

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Section: B Deep Learning Techniquesmentioning

confidence: 99%

SeFACED: Semantic-Based Forensic Analysis and Classification of E-Mail Data Using Deep Learning

et al. 2021

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“…O Yildirim [28] [25] proposed a unified structure to investigate the effects of word embedding and Gated Recurrent Unit (GRU) for text classification on two benchmark datasets included (Google snippets and TREC). GRU is a well-known type of recurrent neural network (RNN), which is the ability to compute sequential data over its recurrent architecture.…”

Section: -Literature Surveymentioning

confidence: 99%

Utilizing Gated Recurrent Units to Retain Long Term Dependencies with Recurrent Neural Network in Text Classification

Chandra¹,

Ahuja²,

Khatri³

et al. 2021

JIST

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The classification of text is one of the key areas of research for natural language processing. Most of the organizations get customer reviews and feedbacks for their products for which they want quick reviews to action on them. Manual reviews would take a lot of time and effort and may impact their product sales, so to make it quick these organizations have asked their IT to leverage machine learning algorithms to process such text on a real-time basis. Gated recurrent units (GRUs) algorithms which is an extension of the Recurrent Neural Network and referred to as gating mechanism in the network helps provides such mechanism. Recurrent Neural Networks (RNN) has demonstrated to be the main alternative to deal with sequence classification and have demonstrated satisfactory to keep up the information from past outcomes and influence those outcomes for performance adjustment. The GRU model helps in rectifying gradient problems which can help benefit multiple use cases by making this model learn long-term dependencies in text data structures. A few of the use cases that follow aresentiment analysis for NLP. GRU with RNN is being used as it would need to retain long-term dependencies. This paper presents a text classification technique using a sequential word embedding processed using gated recurrent unit sigmoid function in a Recurrent neural network. This paper focuses on classifying text using the Gated Recurrent Units method that makes use of the framework for embedding fixed size, matrix text. It helps specifically inform the network of long-term dependencies. We leveraged the GRU model on the movie review dataset with a classification accuracy of 87%.

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“…In [3] the DBN jointly perform with SVM to achieved better results of Chinese text classification algorithm for labeling of semantic role presents CNN in [14]. For classification of long term sentences both are [15,16]. One of the alternates of the traditional CNN is Network In Network (NIN) proposed by [17], where the 1*1 " Conv-filter used is a Multi-Layer Perceptron (MLP) instead of the conventional linear filters Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752 …”

Section: Related Workmentioning

confidence: 99%

A comparative review on deep learning models for text classification

Zulqarnain

Ghazali

Hassim

et al. 2020

IJEECS

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<p>Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic guidance about the deep learning models that which models are best for the task of text classification.</p>

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Efficient processing of GRU based on word embedding for text classification

Cited by 61 publications

References 20 publications

SeFACED: Semantic-Based Forensic Analysis and Classification of E-Mail Data Using Deep Learning

SeFACED: Semantic-Based Forensic Analysis and Classification of E-Mail Data Using Deep Learning

Utilizing Gated Recurrent Units to Retain Long Term Dependencies with Recurrent Neural Network in Text Classification

A comparative review on deep learning models for text classification

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