huyquangdao/cdr_re

Pytorch implementation of Graph State LSTM for CID relation extraction

Pytorch Implementation for Document-level Relation Extraction with Graph State LSTM and Contextual Word Embedding

Introduction

We address the problem of Document-Level N-ary Relation Extraction with a graph-based Long-short term memory network that utilizes a unified dependency-structure of a document, combined with state of the art pre-trained contextual embedding for the Biomedical Domain.

Our model was trained in a end-to-end manner and use whole information from all mention pairs in the document to make the final prediction

Dataset

We use BioCreative5 CDR to train, develop and evaluate our model. The CDR5 dataset contains 1500 documents (500 for training, 500 for development, and 500 for testing) annotated in Pubtator format, all entities relation was labeled at abstract-level instead of mention-level. We train our model with the training set and utilize the dev set to find the best parameters, then we use both the training set and dev set to train our model, and finally, we evaluate our model on the test set.

Training

Please intall all prerequisite packages via requirements file

    pip install -r requirements.txt

All configurations of our model was decribed in the config.json file. To train our model, you can run the following command.

    cd src
    python train.py --config ./data/config.json

Result

	Precision	Recall	F1
Our Model	52.41	71.51	60.35
Our Model + NER	60.09	64.54	62.23

cdr_re's Issues

config中的pkl文件

你好，我想问下config里面的pkl文件是预训练的elmo词向量文件，还是基于文本训练后的得到的文件，想问下能否提供下content里面的文件，不胜感谢