Material named entity recognition (MNER) for knowledge-driven materials using deep learning approach
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 fo...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Science and Business Media Deutschland GmbH
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39073/ 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 |
| _version_ | 1848825676096864256 |
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| author | Miah, Md Saef Ullah Junaida, Sulaiman |
| author_facet | Miah, Md Saef Ullah Junaida, Sulaiman |
| author_sort | Miah, Md Saef Ullah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:32:42Z |
| format | Conference or Workshop Item |
| id | ump-39073 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:32:42Z |
| publishDate | 2023 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-390732023-11-14T03:29:35Z http://umpir.ump.edu.my/id/eprint/39073/ Material named entity recognition (MNER) for knowledge-driven materials using deep learning approach Miah, Md Saef Ullah Junaida, Sulaiman QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) 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. Springer Science and Business Media Deutschland GmbH 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39073/1/Material%20Named%20Entity%20Recognition%20%28MNER%29%20for%20Knowledge-Driven%20Materials.pdf pdf en http://umpir.ump.edu.my/id/eprint/39073/2/Material%20named%20entity%20recognition%20%28MNER%29%20for%20knowledge-driven%20materials_ABS.pdf 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 (Published) https://doi.org/10.1007/978-981-19-9483-8_17 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Miah, Md Saef Ullah Junaida, Sulaiman Material named entity recognition (MNER) for knowledge-driven materials using deep learning approach |
| title | Material named entity recognition (MNER) for knowledge-driven materials using deep learning approach |
| title_full | Material named entity recognition (MNER) for knowledge-driven materials using deep learning approach |
| title_fullStr | Material named entity recognition (MNER) for knowledge-driven materials using deep learning approach |
| title_full_unstemmed | Material named entity recognition (MNER) for knowledge-driven materials using deep learning approach |
| title_short | Material named entity recognition (MNER) for knowledge-driven materials using deep learning approach |
| title_sort | material named entity recognition (mner) for knowledge-driven materials using deep learning approach |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/39073/ http://umpir.ump.edu.my/id/eprint/39073/ 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 |