Relation Extraction of Chinese Fundamentals of Electric Circuits Textbook Based on CNN

Abstract

Deep neural network has been widely used in a variety of natural language processing (NLP) tasks nowadays. As one of the most import research areas, entity relation extraction applies usual recurrent neural networks (RNNs) and convolutional neural networks (CNNs) and has achieved good results. Most relation extraction tasks are about public and general datasets, they are usually natural languages or daily conversations, and have millions of samples, very few relates to small corpus in a specific field. We hope to construct a knowledge graph about Chinese fundamentals of electric circuits textbook for beginners. The knowledge graph shows students knowledge navigation and consists of important concepts about this field and logical relationships between them. To achieve the goal, the first step is to ensure entities and extract entity relationships from raw corpus automatically. In this paper, a relation extraction dataset is built from Chinese fundamentals of electric circuits textbook artificially and research the relation extraction performance of improved position-enhanced CNN model on this task. The experiment result validates the effectiveness of CNN on specific Chinese small corpus relation extraction task.