DEEPCORP: A DETECTION NETWORK FOR REQUIREMENTS ENTITY COREFERENCE BASED ON CONTEXT AND REQUIREMENTS OPERATION ROLE

Automatically detecting the requirement entity coreference (EC) is very important for the consistency analysis of the requirement quality. Existing methods often use edit distance or word embedding to complete EC detection, which does not perform well in capturing complex semantic information of requirement sentences without a large number of expert-labeled data. This paper proposes a new type of deep network Deep & Context-wise & Requirements Operation Role Network (DeepCorp) for EC detection. It introduced entity context and requirement operation role information, and multi-layer perceptron (MLP) was used to implicitly fuse embedded representation to realize the in-depth semantic expression of the requirement entity, so as to judge the semantic similarity of the entity. Experiments on the open requirement document repository show that DeepCorp can achieve the precision of 96.72%, the recall of 96.67% and the F1 value of 96.69%, which has an average increase of 1.27% compared with the existing methods.