Feng Yuntian, Zhang Hongjun, Hao Wenning, Chen Gang
Institute of Command Information System, PLA University of Science and Technology, Nanjing, Jiangsu 210007, China.
Comput Intell Neurosci. 2017;2017:7643065. doi: 10.1155/2017/7643065. Epub 2017 Aug 14.
We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ -Learning algorithm to get control policy in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.
我们使用强化学习和深度学习从非结构化文本中同时提取实体和关系。对于强化学习，我们将该任务建模为一个两步决策过程。深度学习用于自动从非结构化文本中捕获最重要的信息，这些信息代表决策过程中的状态。通过设计每一步的奖励函数，我们提出的方法可以将实体提取的信息传递给关系提取，并获得反馈，以便同时提取实体和关系。首先，我们使用双向长短期记忆网络（bidirectional LSTM）对上下文信息进行建模，实现初步的实体提取。在提取结果的基础上，基于注意力的方法可以表示包含目标实体对的句子，以生成决策过程中的初始状态。然后我们使用树状长短期记忆网络（Tree-LSTM）来表示关系提及，以生成决策过程中的过渡状态。最后，我们采用强化学习算法在两步决策过程中获得控制策略。在ACE2005上的实验表明，我们的方法比现有最先进的方法具有更好的性能，召回率得分提高了2.4%。
