基于网络嵌入和预训练模型的义原预测OA北大核心CSTPCD
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0.623 7,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.
白宇;王之光;刘懿萱;蔡东风
南京航空航天大学计算机科学与技术学院 江苏南京 211106||沈阳航空航天大学人机智能研究中心 辽宁沈阳 110136沈阳航空航天大学人机智能研究中心 辽宁沈阳 110136
计算机与自动化
义原预训练语言模型网络嵌入
SememePre-training language modelNetwork embedding
《计算机应用与软件》 2024 (007)
42-48 / 7
国家自然科学基金项目(U1908216).
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