基于网络嵌入和预训练模型的义原预测

SEMEME PREDICTION BASED ON NETWORK EMBEDDING AND PRE-TRAINING MODEL

义原是构成《知网》概念描述的核心部件,义原预测是HowNet自动或半自动扩展中涉及的关键问题之一。提出一种基于网络嵌入和预训练模型的义原预测方法,通过对《知网》中的字-词-义项-义原及其关系的表示学习,融合预训练语言模型动态构建局部“义项-义原”关系网络,实现新概念与候选义原的动态匹配。实验结果中的义原预测F1值达到0.6237,表明该方法能够更有效地解决《知网》中未登录词的义原预测问题。

Sememe is the core component of concept description in HowNet,and the predication of sememe description for new concepts is the key issue involved in automatic or semi-automatic expansion of HowNet.This paper proposes a sememe prediction method based on network embedding and the pre-training models.It realized the dynamic matching between the new concept and the candidate sememe by learning representation of the character-word-concept-sememe and their relationships in HowNet,and combining the pre-training language models to construct the partial"concept-sememe"relationship network.The predicted F1 value of the experimental results was 0.6237,which indicated that this method could solve the problem of semantic prediction of OOV words in HowNet more effectively.