A Baseline System For CCKS-2019-IPRE
We provide a baseline system based on convolutional neural network with selective attention.
- python 3.6
- tensorflow 1.12.0
Step 1: Download data
Please download the data from the competition website, then unzip files and put them in
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.
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.
- 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.