基于BERT的因果关系抽取

针对传统关系抽取模型依赖特征工程等机器学习方法, 存在准确率较低且规则较繁琐等问题, 提出一种BERT+BiLSTM＋CRF方法. 首先使用BERT(bidirectional encoder representations from transformers)对语料进行预训练; 然后利用BERT根据上下文特征动态生成词向量的特点, 将生成的词向量通过双向长短期记忆网络（BiLSTM）编码； 最后输入到条件随机场（CRF）层完成对因果关系的抽取. 实验结果表明, 该模型在SemEval-CE数据集上准确率比BiLSTM+CRF+self-ATT模型提高了0.054 1, 从而提高了深度学习方法在因果关系抽取任务中的性能.

Causality Extraction Based on BERT

Aiming at the problem that the traditional relation extraction model relied on machine learning methods such as feature engineering, which had low accuracy and complicated rules, we proposed a BERT+BiLSTM+CRF method. Firstly,  bidirectional encoder representations from transformers (BERT) was used to pre-train the corpus. Secondly, by using BERT to dynamically generate word vectors according to context features, the generated word vectors were encoded through bidirectional long and short-term memory network (BiLSTM). Finally, we input it into the conditional random field (CRF) layer to complete the extraction of causality. The experimental result shows that the accuracy of the model on the SemEval-CE dataset is 0.054 1 higher than that of the BiLSTM+CRF+self-ATT model, which improves the performance of the deep learning method in the task of causality extraction.