Skip to main content

Table 2 Hyper-parameter settings

From: A neural joint model for entity and relation extraction from biomedical text

Type

Hyper-parameter

Training

α=0.03,λ=10−8

Embedding

dim(emb(w i ))=200

 

\(dim(emb(p_{i}), emb(d_{i})~\text {or}~emb(l^{e}_{i}))=25\)

CNN

dim(emb(c))=25,C=3

 

dim(r w )=25

Bi-LSTM-RNN (Entity)

\(dim(\overrightarrow {h}_{i})\) or \(dim(\overleftarrow {h}_{i})=100\)

 

\(dim(h^{e}_{i})=100\)

Bi-LSTM-RNN (Relation)

dim(↑ h a ,↑ h b ,↓ h a or ↓ h b )=100

 

dim(h r)=100

  1. dim denotes vector dimensions and emb denotes feature embeddings