Article Classification using Natural Language Processing and Machine Learning

Điện, Trần Thanh; Loc, Bui Huu; Nguyen, Thin

doi:10.1109/acomp.2019.00019

Cited by 17 publications

(5 citation statements)

References 2 publications

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“…Previous work [13] we showed that SVM works very well for automatic topic classification in an online submission system, however, in this work we have continued to improve and showed that using Deep Learning approach the results even better. Since the data sets are imbalanced, we report the AUC instead of the accuracy metric as Table 2.…”

Section: Topic Classification Results Of Various Machine Learning Algmentioning

confidence: 62%

“…A collection of Turkish news and articles dataset can be downloadable at UCI repository 5 including 3600 samples on 6 categories. The Scientific articles of a university and VnExpress Newsletters in Vietnam were used in our previous work at ACOMP 2019 [13] include 650 samples, 3431 features and 10000 samples, 3266 features, respectively. The considered numbers of classes also vary from binary classification to 10-class classification.…”

Section: Data Descriptionmentioning

confidence: 99%

“…From the predicted category, editors can find appropriate reviewers for the submission faster and help to speed up the review process. This paper is an extension of work originally presented in the International Conference on Advanced COMPuting and Applications (ACOMP) 2019 in Nha Trang, Vietnam [13]. We present an approach using machine learning algorithms to classify automatically the articles which were submitted via an online submission system.…”

Section: Introductionmentioning

confidence: 99%

“…For this procedure, we can use the natural language processing techniques to pre-process the data before fetching them into a machine learning algorithm to do the classification tasks. Comparing to the work in [13], this study provides contributions as follows:…”

Section: Introductionmentioning

confidence: 99%

“…• For experiments, we have collected 5 data sets (instead of 2 Vietnamese data sets as in [13]). These data sets represent for 3 languages (English, Turkey, and Vietnamese) and have multi-classes (2, 4, 6, 9, and 10 classes as presented in Table 1) • In this work, since the topic classes are imbalanced, we have used AUC (Area Under the ROC Curve) as a metric for comparison.…”

Section: Introductionmentioning

confidence: 99%

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

Điện¹,

Thanh-Hai²,

Nguyen³

2020

Adv. sci. technol. eng. syst. j.

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Topic classification is a crucial task where knowledge categories exist within hierarchical information systems designed to facilitate knowledge search and discovery. An application of topic classification is article (e.g., journal/conference paper) classification which is very useful for online submission systems. In fact, numerous online journals/magazine submission systems usually receive thousands of article submissions or even more for each month. This leads to a huge amount of time-consumption of editors to process and categorize the submissions aiming to look for and assign appropriate reviewers to the submitted articles. In this study, we propose an approach based on natural language processing techniques and machine learning algorithms (both classic machine learning and deep learning) to automatic classify the topics of articles in an online submission system. We show by promising performance collected from prediction tasks to present that the proposed method is a potential approach for applying to the real system.

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Section: Topic Classification Results Of Various Machine Learning Algmentioning

confidence: 62%

Section: Data Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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