Abstract

When searching for scientific research articles, there is often an abundance of search results from various sources, which makes it difficult to determine the relevance between topics. This encourages the use of entity matching techniques and three deep learning methods: RNN, Attention, and Hybrid methods. We utilize a deep learning approach that combines RNN, Attention, and Hybrid models to improve entity matching capabilities. With this approach, a dataset of scientific articles based on the title, author, and keywords can be processed, and the RNN model can be used to learn the representation of those features separately. Furthermore, the Attention model is used to derive the relevant weights between pairs of features, while the hybrid model combines the results from the RNN and Attention models. In this context, the combination of RNN, Attention, and Hybrid methods can be a solution in determining relevance between scientific articles, improving efficiency, and effectiveness in research literature search. Based on the results of the analysis, the highest results were obtained in the hybrid model, with an F1 score accuracy of 51.18%.

Original languageEnglish
Title of host publication2023 5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350369489
DOIs
Publication statusPublished - 2023
Event5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023 - Hybrid, Pangkalpinang, Indonesia
Duration: 6 Oct 20237 Oct 2023

Publication series

Name2023 5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

Conference

Conference5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023
Country/TerritoryIndonesia
CityHybrid, Pangkalpinang
Period6/10/237/10/23

Keywords

  • DeepMatcher
  • computer vision
  • entity matching
  • research article

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