This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading. Traditional methods that exploit knowledge from KBs encode knowledge as discrete indicator features. Not only do these features generalize poorly, but they require task-specific feature engineering to achieve good performance. We propose KBLSTM, a novel neural model that leverages continuous representations of KBs to enhance the learning of recurrent neural networks for machine reading. To effectively integrate background knowledge with information from the currently processed text, our model employs an attention mechanism with a sentinel to adaptively decide whether to attend to background knowledge and which information from KBs is useful. Experimental results show that our model achieves accuracies that surpass the previous state-of-the-art results for both entity extraction and event extraction on the widely used ACE2005 dataset.

本文介绍如何使用外部知识库，提出了一种用于改进机器阅读的循环神经网络的新型神经模型KBLSTM，并使用具有哨兵的注意机制来自适应地决定是否关注背景知识以及哪些知识对于信息提取是有用的，实验结果显示，该模型在广泛使用的ACE2005数据集上实现了准确性，超过了之前最先进的结果。

利用知识库提升LSTM的机器阅读能力