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Table 2 Tuned hyper-parameter of the proposed model

From: Exploiting document graphs for inter sentence relation extraction

Information

Configuration

Parameters

Dependency embeddings

Dependency type

LUT \(\mathbf {W}^{e}_{typ}\) size 72×150

10800

 

Dependency direction

LUT \(\mathbf {W}^{e}_{dir}\) size 2×150

300

Token embeddings

FastText embeds

Pre-trained 300−dim vector

−

 

Character embeddings

LUT \(\mathbf {W}^{e}_{c}\) size 85×50

4250

  

biLSTM with 50 units

40400

 

POS tag

LUT \(\mathbf {W}^{e}_{t}\) size 57×50

2850

 

WordNet embeds

Fixed spare 45−dim vector

−

Augmented information

Base distance embeds

32−dim vector

32

 

Self attention score

We,be transform from 832 dim to scalar

833

 

Heuristic attention

Linear

−

 

Kernel filters

100 filters size 832×1

83300

Shared weight-CNN

128 filters each region-size (1,2,3)

2056320

Classifier

Fully-connected MLP

Do not use

−

 

Softmax

2 classes

768

Total number of parameters

2199853

  1. Embed: Embedding, Dim: Dimension