About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Addressing Semantic Challenges towards Data Mining using Natural Language Processing |
Author(s) |
Amit K. Verma, Zhisong Zhang, Benjamin M. Glaser, Robin Kuo, Jason Zhang, Nicholas David, Emma Strubell, Anthony D. Rollett |
On-Site Speaker (Planned) |
Amit K. Verma |
Abstract Scope |
Data problems persists across many disciplines of materials science, with a particular dearth for high temperature materials where most material attributes need to be determined experimentally. To address this challenge, we are working on two key ideas: 1) data retrieval; and 2) recognition systems for identifying key concepts and their dependencies, from published literature. The first aim to address the lack of open-access experimental data for various machine learning activities, while the second aim to encode the semantics of the domain for bridging various heterogenous data sources. Natural Language Processing (NLP) provides a host of solutions in this regard, and this talk focuses on how NLP is being used to develop the tools mentioned, with specific examples to support our vision. This includes, but is not limited to, BERT language models for entity resolution, conditional random field models for entity extraction, etc. |
Proceedings Inclusion? |
Planned: |
Keywords |
High-Temperature Materials, Machine Learning, |