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

• Main Authors: Miah, Md Saef Ullah, Junaida, Sulaiman
• Format: Conference or Workshop Item
• Language: English; English
• Published: Springer Science and Business Media Deutschland GmbH 2023
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/39073/1/Material%20Named%20Entity%20Recognition%20%28MNER%29%20for%20Knowledge-Driven%20Materials.pdf; http://umpir.ump.edu.my/id/eprint/39073/2/Material%20named%20entity%20recognition%20%28MNER%29%20for%20knowledge-driven%20materials_ABS.pdf; http://umpir.ump.edu.my/id/eprint/39073/; https://doi.org/10.1007/978-981-19-9483-8_17

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.