融合特征编码和短语交互感知的隐式篇章关系识别OA北大核心CSTPCD

Implicit discourse relation recognition is a challenging task because of its difficulty and universality.From the perspective of argument coding and argument interaction,an implicit discourse relation recognition model integrating feature coding and phrase interaction perception is proposed.The model considers both the characteristics of argument it-self and the interaction characteristics between arguments,and optimizes separately.The part of argument coding incorpo-rates bidirectional long short-term memory(BiLSTM)and recurrent attention convolution neural network(RACNN),which can capture global and local features of arguments in a more comprehensive way;in the part of argument interaction,the se-mantic relationship between arguments is modeled from phrase level,and a mechanism of phrase-level interactive attention is constructed.Also,neural tensor network(NTN)is used to dig into the relational pattern,which can better reflect the po-tential deeper relational relationship between arguments.Experimental results on penn discourse treebank(PDTB)dataset show that the F1 values of this model are superior to other comparison models.