A material named entity recognition model using domain embedded bi-lstm and hybrid cosine with wordvector similarity approach
In recent years, the field of energy storage has witnessed a significant surge in research activities. Concurrently, there is a growing demand for the exploration of materials and processes employed in supercapacitors. Given the multitude of available materials and processes, it is difficult to expe...
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| Format: | Thesis |
| Language: | English |
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2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44642/ http://umpir.ump.edu.my/id/eprint/44642/1/A%20material%20named%20entity%20recognition%20model%20using%20domain%20embedded%20bi-lstm%20and%20hybrid%20cosine%20with%20wordvector%20similarity%20approach.pdf |