摘要
该文研究了基于HowNet的KDML语法体系的术语DEF自动生成问题,提出一种基于树形解码器的生成方法。在编码器端输入专业术语以及其他外部信息(术语的定义、术语子词的义原等);在解码器端交替使用义原解码器和关系解码器,同时使用注意力机制关注编码器端的各种表征信息,最终得到“义原-关系-义原”形式的输出,并组合成术语对应的义原树,进而得到术语的DEF表示以辅助专业领域HowNet的构建,最终取得了首义原F_(1)值74.13%、总义原F_(1)值53.92%、总关系F_(1)值53.33%、总三元组F_(1)值30.48%的结果。
This paper investigates the automatic generation of DEF based on KDML of HowNet,and proposes a generation method based on tree-structured decoder.The inputs of the encoder are technical terms and other external information(definition of the terms,sememes of sub-words of the terms,etc.).As for decoding,sememe decoder and role decoder are used alternately,and attention mechanism is used to capture various representation information.Finally,the output in the form of"sememe-role-sememe"is obtained,which is combined into the sememe tree corresponding to terms to finalize the DEF representation of terms in HowNet.Experimental results show that the proposed method achieves 74.13%F_(1)-value for the first sememe generation,53.92%for the overall sememe generation,53.33%for the role generation and 30.48%for the overall triple generation.
作者
吕嘉
王裴岩
蔡东风
张桂平
李林娜
LYU Jia;WANG Peiyan;CAI Dongfeng;ZHANG Guiping;LI Linna(Human-Computer Intelligence Research Center,Shenyang Aerospace University,Shenyang,Liaoning 110136,China)
出处
《中文信息学报》
CSCD
北大核心
2024年第6期24-33,共10页
Journal of Chinese Information Processing
基金
国家自然科学基金(U1908216)
辽宁省重点研发计划(2019JH2/10100020)。
关键词
知网
DEF生成
树形结构解码
HowNet
DEF generation
tree-structured decoder
