Regularized training of compositional distributional semantic models

Xuefeng Yang, Kezhi Mao, Rui Zhao

Abstract: The compositional distributional semantic models (cDSMs) aim to use numerical vectors to represent the meaning of complex language expressions. cDSMs are usually trained using single training target, either from the basic DSM or a pseudo gold standard. In this paper, a new regularized training approach that integrates multiple training targets is proposed to improve semantic composition models. The experiment results show that the proposed training algorithm can effectively enhance compositional distributional semantic models.