Sememe is the core component that constitutes the conceptual description of words in HowNet,and the recommendation of sememes for describing new words or concepts is crucial for the automatic or semi-automatic ex-tension of HowNet.Focusing on the sememe recommendation of new words,this paper proposes a sememe attention enhanced pre-training language model named SaBERT.To estimate the similarity between a new word and an in-vocabulary word of HowNet,we employ the existing concepts of the in-vocabulary word to describe the atten-tion distribution of the sememe sequence,and optimize the BERT+CNN model with an objective of similarity iso-morphism.Experimental results show that SaBERT achieves achieve 0.831 4,0.800 7 and 0.815 8 for precision,re-call and F1 value,respectively.
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