Material named entity recognition (MNER) for knowledge-driven materials using deep learning approach

Miah, Md Saef Ullah and Junaida, Sulaiman (2023) Material named entity recognition (MNER) for knowledge-driven materials using deep learning approach. In: Lecture Notes in Networks and Systems; 4th International Conference on Trends in Cognitive Computation Engineering, TCCE 2022 , 17-18 December 2022 , Tangail. pp. 1-10., 618 (295659). ISSN 2367-3370 ISBN 978-981199482-1

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The scientific literature contains an abundance of cutting-edge knowledge in the field of materials science, as well as useful data (e.g., numerical values from experimental results, properties, and structure of materials). To speed up the identification of new materials, these data are essential for data-driven machine learning (ML) and deep learning (DL) techniques. Due to the large and growing amount of publications, it is difficult for humans to manually retrieve and retain this knowledge. In this context, we investigate a deep neural network model based on Bi-LSTM to retrieve knowledge from published scientific articles. The proposed deep neural network-based model achieves an F1 score of 9 ~ 7 % for the Material Named Entity Recognition (MNER) task. The study addresses motivation, relevant work, methodology, hyperparameters, and overall performance evaluation. The analysis provides insight into the results of the experiment and points to future directions for current research.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Bi-LSTM; EDLC; Material named entity recognition; Materials science; Named entity recognition
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division: College of Engineering
Faculty of Computing
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 14 Nov 2023 03:29
Last Modified: 14 Nov 2023 03:29
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