End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
复制标题
DOI：
10.18653/v1/p16-1105
复制
发表时间：
2016-01
期刊：
影响因子：
--
通讯作者：
Makoto Miwa;Mohit Bansal
中科院分区：
文献类型：
--
作者：
Makoto Miwa;Mohit Bansal
我们提出了一种新的端到端神经模型来提取实体及其之间的关系。我们的基于递归神经网络的模型通过将双向树形结构的LSTM-RNN堆叠在双向顺序LSTM-RNN上来捕捉单词序列和依存树子结构信息。这允许我们的模型在单个模型中联合表示具有共享参数的实体和关系。我们还鼓励在训练期间检测实体，并通过实体预训练和计划抽样在关系提取中使用实体信息。该模型在端到端关系提取方面较现有的基于特征的模型进行了改进，在ACE2005和ACE2004上的F1-Score的相对误差分别降低了12.1%和5.7%。我们还表明，我们的基于LSTM-RNN的模型在名义关系分类(SemEval-2010任务8)上优于最先进的基于CNN的模型(F1-Score)。最后，我们给出了一个广泛的烧蚀分析几个模型组件。
We present a novel end-to-end neural model to extract entities and relations between them. Our recurrent neural network based model captures both word sequence and dependency tree substructure information by stacking bidirectional tree-structured LSTM-RNNs on bidirectional sequential LSTM-RNNs. This allows our model to jointly represent both entities and relations with shared parameters in a single model. We further encourage detection of entities during training and use of entity information in relation extraction via entity pretraining and scheduled sampling. Our model improves over the state-of-the-art feature-based model on end-to-end relation extraction, achieving 12.1% and 5.7% relative error reductions in F1-score on ACE2005 and ACE2004, respectively. We also show that our LSTM-RNN based model compares favorably to the state-of-the-art CNN based model (in F1-score) on nominal relation classification (SemEval-2010 Task 8). Finally, we present an extensive ablation analysis of several model components.