Data for the shared task is available at https://github.com/SUDA-HLT/IPRE, and the review paper is available at https://arxiv.org/abs/1908.11337.

A Baseline System For CCKS-2019-IPRE

Introduction

We provide a baseline system based on convolutional neural network with selective attention.

Getting Started

Environment Requirements

• python 3.6
• numpy
• tensorflow 1.12.0

Step 1: Download data

Please download the data from the competition website, then unzip files and put them in ./data/ folder.

Step 2: Train the model

You can use the following command to train models for Sent-Track or Bag-Track:

python baseline.py --level sent 
python baseline.py --level bag

The model will be stored in ./model/ floder. We provide large scale unmarked corpus for train word vectors or language mdoels. The word vectors used in baseline system are trained by a package named gensim in python, and some parameters are set as follows:

from gensim.models import word2vec
model = word2vec.Word2Vec(sentences, sg=1, size=300, window=5, min_count=10, negative=5, sample=1e-4, workers=10)

Step 3: Test the model

You can use the following command to test models for Sent-Track or Bag-Track:

python baseline.py --mode test --level sent 
python baseline.py --mode test --level bag

Predicted results will be stored in result_sent.txt or result_bag.txt.

Evaluation

We use f1 score as the basic evaluation metric to measure the performance of systems. In our baseline system, we get about 0.22 f1 score in Sent-track and about 0.31 f1 score in Bag-Track by using pre-trained word vectors.

References

• Wang H, He Z, Zhu T, et al. CCKS 2019 Shared Task on Inter-Personal Relationship Extraction[J]. arXiv preprint arXiv:1908.11337, 2019.
• Lin Y, Shen S, Liu Z, et al. Neural relation extraction with selective attention over instances[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 1: 2124-2133.