Table 2 The main hyper-parameters of our model
From: A neural network approach to chemical and gene/protein entity recognition in patents
Hyper-parameter | Value | Values tested |
|---|---|---|
Word embedding dimension | 100 | 50, 100, 200 |
Character embedding dimension | 25 | 25, 50 |
Character-level BiLSTM state size | 25 | 25, 50 |
Capitalization embedding dimension | 5 | 5, 10 |
POS embedding dimension | 25 | 25, 50 |
Chunking embedding dimension | 10 | 10, 20 |
NER embedding dimension | 5 | 5, 10 |
Word-level BiLSTM state size | 100 | 50, 100, 200 |
SGD learning rate | 0.001 | 0.01, 0.005, 0.001 |