Abstract: Information extraction is a technique of extracting specific information from textual data.It has been widely used in knowledge graph,information retrieval,question answering system,sentiment analysis and text mining.As the core task and important part of information extraction,relation classification can realize the recognition of semantic relations between entities.In recent years,deep learning has made remarkable achievements in relation extraction tasks.So far,researchers have focused their efforts on improving neural network models,but there is still a lack of effective methods to obtain cross-sentence semantic information from paragraphs or discourse level texts with close semantic relationships between different sentences.However,semantic relationships between sentences for relation extraction tasks are of great use.In this paper,for such paragraphs or discourse level relation extraction datasets,a method to combine sentences with their cross-contextual information as the input of the neural network model is proposed,so that the model can learn more semantic information from paragraphs or discourse level texts.Cross-sentence contex tual information is introduced into different neural network models,and experiments are carried out on two relation classification datasets in different fields including San Wen dataset and Policy dataset.The effects of cross-sentence contex-tual information on model accuracy are compared.The experiment show that the proposed method can effectively improve the performance of relation classification models including Convolutional neural network,bidirectional long short-term memory network,attention-based bidirectional long short-term memory network and convolutional recurrent neural network.In addition,this paper proposes a relation classification dataset named Policy based on the texts of policies and regulations in the field of four social insurance and one housing fund,which is used to verify the necessity of introducing cross-sentence contextual information into the relation classification tasks in some practical fields.

Key words: Cross-sentence contextual information, Four social insurance and one housing fund, Neural network, Relation classification, Sentence similarity