TY - GEN
T1 - Research Article Matching Methods using Attention, Hybrid, RNN, and SIF
AU - Sulastri, Miftakhul Janah
AU - Ardan, Indira Salsabila
AU - Rakhmawati, Nur Aini
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the digital era, search engines such as Google Scholar make it easier to access scientific information, but there are challenges in determining the relevance and accuracy of articles. One technique used to select scientific articles relevant to a particular topic is entity matching. This study aims to analyze the performance of entity matching using four models owned by DeepMatcher. The models include Attention, Hybrid, RNN, and SIF, which are applied to a dataset of articles with keyword fairness in AI. The collected dataset consisted of 50 articles retrieved from Publish or Perish 8. The dataset was preprocessed up to pairing, resulting in a final dataset of 1225 rows and eight columns. The columns follow the column types that must be present in the dataset that will be processed by DeepMatcher, namely the "left,""right,""label,"and "ID"attributes. Based on the results of the analysis, it was found that the most suitable DeepMatcher model, namely the RNN model, had the highest average F1 score of 54.08%.
AB - In the digital era, search engines such as Google Scholar make it easier to access scientific information, but there are challenges in determining the relevance and accuracy of articles. One technique used to select scientific articles relevant to a particular topic is entity matching. This study aims to analyze the performance of entity matching using four models owned by DeepMatcher. The models include Attention, Hybrid, RNN, and SIF, which are applied to a dataset of articles with keyword fairness in AI. The collected dataset consisted of 50 articles retrieved from Publish or Perish 8. The dataset was preprocessed up to pairing, resulting in a final dataset of 1225 rows and eight columns. The columns follow the column types that must be present in the dataset that will be processed by DeepMatcher, namely the "left,""right,""label,"and "ID"attributes. Based on the results of the analysis, it was found that the most suitable DeepMatcher model, namely the RNN model, had the highest average F1 score of 54.08%.
KW - Attention
KW - DeepMatcher
KW - Entity Matching
KW - Hybrid
KW - Recurrent Neural Network (RNN)
KW - Smooth Inverse Frequency (SIF)
UR - http://www.scopus.com/inward/record.url?scp=85181133314&partnerID=8YFLogxK
U2 - 10.1109/IEIT59852.2023.10335520
DO - 10.1109/IEIT59852.2023.10335520
M3 - Conference contribution
AN - SCOPUS:85181133314
T3 - Proceedings - IEIT 2023: 2023 International Conference on Electrical and Information Technology
SP - 120
EP - 125
BT - Proceedings - IEIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Electrical and Information Technology, IEIT 2023
Y2 - 14 September 2023 through 15 September 2023
ER -