Automatic work‐order assignment method for Chinese government hotline

Abstract

Abstract Government hotline plays a significant role in meeting the demands of the people and resolving social conflicts in China. In this paper, we propose an automatic work‐order assignment method based on event extraction and external knowledge to address the problem of low efficiency with manual assignment for Chinese government hotline. Our proposed assignment method is composed of four parts: (1) Semantic encoding layer, which extracts semantic information from the work‐order text and obtains semantic representation vectors with contextual feature information. (2) Event extraction layer which extracts the local features and global features from the semantic representation vectors with the help of the CRF network to enhance event extraction effect. (3) External knowledge embedding layer, which integrates ‘rights and responsibilities lists’ with the historical information of the work‐order to assist assignment. (4) Assignment layer which completes work‐order assignment by combining two output vectors from event extraction layer and external knowledge embedding layer. Experimental results show our proposed method can achieve better assignment performance compared with several baseline methods.

Keywords

• event extraction
• external knowledge
• government hotline
• rights and responsibilities lists
• work‐order assignment