Deep Learning Approach for Automatic Topic Classification in an Online Submission System

Điện, Trần Thanh; Thanh-Hai, Nguyen; Nguyen, Thin

doi:10.25046/aj050483

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Previous researches on short text classification [6][7][8][9] showed that deep learning are recommended. Thus, in this experiment we evaluated commonly utilized of deep learning architectures such as LSTM, Bi-LSTM, and CNN with a baseline comparison of non-deep learning approach of logistic regression.…”

Section: Evaluate Classification Performancesmentioning

confidence: 99%

“…Thus LDA alone is not enough for handling short texts. Classification with short texts usually takes deep learning to address the subject context's insufficiency [6,7]. Since graph as a data structure is beneficial in highlighting the subject context, the deep learning approach evolves into a neural network with spatial moves on graph nodes called graph convolutional network (GCN).…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, other works with the topic range extended for different domains required more than short texts to ensure the topic quality [4,5]. Hence, the deep learning approach is convenient for classifying short texts [6][7][8][9]. However, the possibility of overlapped context needs additional information from word concepts of knowledge base [10] or predefined categories of classification system [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Graph Model and Deep Learning for Topic Labels in Classifying Short Texts of Scientific Article Titles

2022

IJIES

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In an article searching system, topic categorization could guide researchers in finding the appropriate documents among the abundant availability of scientific articles. Because they are easily obtained from the internet, there is a preference for using short texts to full-text articles for data collection of the searching system. However, topic label scarcity becomes a problem, especially when preparing the system in a cold-start situation or without predefined topic categories. Typical topic analysis with a statistical-based unsupervised Latent Dirichlet Allocation (LDA) identifies clusters of words or topics based on the word distribution to overcome the label scarcity. For ease of use, words in LDA topics are manually observed, and some are set as topic names. Lower precision happened when tagging other articles using the LDA topics for the searching system preparation with categorization or classification approach. The precision values could be influenced by too many identified LDA topics. Thus, the overlapped context in the LDA results is possible since the same words appear in different topics, leading to many false positives. Here, the instigated problem is making classification results have comparable accuracy and precision values with the existing data condition of no topic labels and overlapped context when identifying topics. The problem solution is motivated to consider the word relations with others when identifying topics to differentiate the word context. Therefore, our contribution is to investigate LDA and relationships between words as a graph with a prevalent neural network model of deep learning called Graph Convolutional Network (GCN) for automatically determining the topics before examining them in classification tasks. Guided by the proposed framework, we synthesize training samples to make the dataset for LDA topics more similar in contexts. The empirical analysis through the experiments has thoroughly evaluated the LDA topics as a baseline to compare the results of statistical based (LDA) and Deep Learning based topic identification (Deep LDA) to ensure the topic quality. Then, we compared the usage of GCN with other frequently used text classifications. The classification results showed that the graph representation used to capture the subject context relationship between keywords in GCN has supported the balance between accuracy and precision.

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Section: Evaluate Classification Performancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graph Model and Deep Learning for Topic Labels in Classifying Short Texts of Scientific Article Titles

2022

IJIES

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NLP based Model for Classification of Complaints: Autonomous and Intelligent System

Quratulain

Shaukat

Saif

2022

2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)

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Efficiency Improvisation of Large-Scale Knowledge Systems in Feature Determination using Proposed HVGAN Architecture

Nagarajan,

Asha Jerlin,

Devarajan

et al. 2024

J. Info. Know. Mgmt.

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Knowledge Management (KM) is crucial for efficient information retrieval and forms the backbone of implementing an Information System (IS). Efficiently extracting meaningful trends from headlines aids in visualising demographic situations and addressing emergency handling needs. Understanding Information Resource Management (IRM) and Knowledge Resource Management (KRM) concepts benefits end users. This proposed KRM concept organises knowledge, ensuring global resource control through a neural decision framework. Traditional methods like Structural Equation Modelling (SEM) have higher computational steps, whereas neural network strategies in KM reduce steps and improve data prediction accuracy. The paucity of input data is a common challenge, which can be overcome by using Generative Adversarial Network (GAN) architecture for probabilistic generative processes. This research evaluates acquisition, spreading, vulnerability, and application using GAN-based knowledge systems that mitigate data scarcity through generative methods. The proposed Hierarchical Vanilla Generative Adversarial Network (HVGAN) utilises gradient functions and hierarchical computation algorithms to achieve desired knowledge extraction with lower time complexity and higher accuracy. Implementation improves efficiency in accessing stored knowledge, yielding an AUC score of 96% compared to other GAN architectures.

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Deep Learning Approach for Automatic Topic Classification in an Online Submission System

Cited by 3 publications

References 26 publications

Graph Model and Deep Learning for Topic Labels in Classifying Short Texts of Scientific Article Titles

Graph Model and Deep Learning for Topic Labels in Classifying Short Texts of Scientific Article Titles

NLP based Model for Classification of Complaints: Autonomous and Intelligent System

Efficiency Improvisation of Large-Scale Knowledge Systems in Feature Determination using Proposed HVGAN Architecture

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