• CNN-relation-extraction
• • Environment Requirements
  • Data
  • Usage
  • Result
  • Reference Link

CNN-relation-extraction

Mindspore Implementation of Relation Classification via Convolutional Deep Neural Network.

Environment Requirements

• python 3.7.5
• mindspore-dev linux

Data

• SemEval2010 Task8 [paper]
• Embedding - Turian et
  al.(2010) [paper]

Usage

1. Download the embedding and decompress it into the embedding folder.
2. Run the following the commands to start the program.

python run.py

More details can be seen by python run.py -h.

3. You can use the official scorer to check the final predicted result.

perl semeval2010_task8_scorer-v1.2.pl proposed_answer.txt predicted_result.txt >> result.txt

Result

Not finished yet.

The training log can be seen in train.log and the official evaluation results is available in result.txt.

Note:

• Some settings are different from those mentioned in the paper.
• No validation set used during training.
• Just complete the part without lexical level features. More detail is available in Section 5.3 in this paper.
• Although I try to set random seeds, it seems that the results of each run are a little different.

Reference Link

• https://github.com/ShomyLiu/pytorch-pcnn
• https://github.com/FrankWork/conv_relation
• https://github.com/onehaitao/CNN-relation-extraction