Improve Research Reproducibility A Bio-protocol resource
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Embeddings

AT Austin Cheng-Yun Tsai
SH Sheng-Yi Hong
LY Li-Hung Yao
WC Wei-Der Chang
LF Li-Chen Fu
YC Yu-Ling Chang
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The embedding layer was used to transform every word into its corresponding word vector. The rationale behind this transformation was to map each word into a high-dimensional space to represent the word with a high-dimensional word vector that contained semantically meaning of the word and enabled further comparison and computation between words. Word vectors could be pre-trained on general language corpus such as Wikipedia to capture the general semantic meaning and the relationship between words of the language. The pre-trained word vectors could provide better representation for each word and offer better semantic meaning than the randomly chosen word vectors.

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