Conference proceeding
Fine-Tuning BERT Model for Materials Named Entity Recognition
2021 IEEE International Conference on Big Data (Big Data), pp 3717-3720
15 Dec 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Scientific literature presents a wellspring of cutting-edge knowledge for materials science, including valuable data (e.g., numerical data from experiment results, material properties and structure). These data are critical for accelerating materials discovery by data-driven machine learning (ML) methods. The challenge is, it is impossible for humans to manually extract and retain this knowledge due to the extensive and growing volume of publications.To this end, we explore a fine-tuned BERT model for extracting knowledge. Our preliminary results show that our fine-tuned Bert model reaches an f-score of 85% for the materials named entity recognition task. The paper covers background, related work, methodology including tuning parameters, and our overall performance evaluation. Our discussion offers insights into our results, and points to directions for next steps.
Metrics
Details
- Title
- Fine-Tuning BERT Model for Materials Named Entity Recognition
- Creators
- Xintong Zhao - Drexel UniversityJane Greenberg - Drexel UniversityYuan An - Drexel UniversityXiaohua Tony Hu - Drexel University
- Publication Details
- 2021 IEEE International Conference on Big Data (Big Data), pp 3717-3720
- Conference
- 2021 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Number of pages
- 1
- Grant note
- Office of Advanced Cyberinfrastructure (10.13039/100000105) National Science Foundation (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000800559503112
- Scopus ID
- 2-s2.0-85125299798
- Other Identifier
- 991019168993804721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods