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
白宇 1王之光 2刘懿萱 2蔡东风1
作者信息
- 1. 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
- 2. 沈阳航空航天大学人机智能研究中心 辽宁沈阳 110136
- 折叠
摘要
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.
Abstract
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.
关键词
义原/预训练语言模型/网络嵌入Key words
Sememe/Pre-training language model/Network embedding引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据