关键词: 自然语言处理, 实体关系提取, 胶囊网络（CapsuleNet）, 位置感知能力, PPCNet

Abstract:

Most of the current relation extraction methods apply Convolutional Neural Network（CNN） and Recurrent Neural Network（RNN） methods. However, CNN and RNN both express their features in scalar form, which leads to their poor sensitivity to location information. Besides, the maximum pooling of CNN and RNN results in the loss of feature information. Aiming at these two problems, Position Perception CapsuleNet（PPCNet） has been structured. Capsule is a group of neurons whose feature expression is based on vector form. PPCNet transforms the position relation ship between words into position embedding and integrates it into capsule to obtain position perception ability. Furthermore, PPCNet uses dynamic routing instead of pooling to reduce feature loss and gets 82.84% F1 value on SemEval-2010task8 dataset.

Key words: Natural Language Processing（NLP）, relation extraction, CapsuleNet, position perception, Position Perception CapsuleNet（PPCNet）