目前常用的基于深度学习的有监督关系抽取方法.zip

# 复现CNN 和 lstm+att 做关系抽取 ##实验结果 ### cnn Micro-averaged result (excluding Other): P = 2004/2262 = 84.36% R = 2004/2449 = 79.23.% F1 = 81.18% MACRO-averaged result (excluding Other): P = 83.66% R = 77.15% F1 = 80.72% ### a-cnn #### 此结果运行代码 https://github.com/lawlietAi/pytorch-acnn-model 所得 Micro-averaged result (excluding Other): P = 2004/2262 = 85.59% R = 2004/2449 = 79.83% F1 = 82.78% MACRO-averaged result (excluding Other): P = 85.66% R = 78.15% F1 = 81.72% ### lstm+att Micro-averaged result (excluding Other): P = 2004/2262 = 83.59% R = 2004/2449 = 79.83% F1 = 81.75% MACRO-averaged result (excluding Other): P = 82.66% R = 79.15% F1 = 80.72% # Usage ## Train #! /bin/bash mkdir -p saved_models CUDA_VISIBLE_DEVICES=2 python3 src/train.py --num_epochs=200 --word_dim=50 python src/train.py --num_epochs=200 --word_dim=50 --test src/scorer.pl data/results.txt data/test_keys.txt python src/train.py --num_epochs=200 --word_dim=300 python src/train.py --num_epochs=200 --word_dim=300 --test # Data SemEval-2010 Task #8 # Reference - Relation Classification via Convolutional Deep Neural Network (COLING 2014), D Zeng et al. - Relation Extraction: Perspective from Convolutional Neural Networks (NAACL 2015), TH Nguyen et al. - Zhou. (2016). Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification. ACL - https://github.com/lawlietAi/pytorch-acnn-model - https://github.com/FrankWork/conv_relation