基于概念链和图注意力网络的下位词扩展  

Hyponym Expansion Based on Concept Chains and Graph Attention Networks

作　　者：王煜栋 白宇 叶娜 陈建军 Wang Yudong;Bai Yu;Ye Na;Chen Jianjun(College of Computer Science,Shenyang Aerospace University,Shenyang 110136,China;Shenyang Northern Software College of Information Technology,Shenyang 110136,China)

机构地区：[1]沈阳航空航天大学计算机学院,沈阳110136 [2]沈阳北软信息职业技术学院,沈阳110136

出　　处：《数据分析与知识发现》2024年第11期136-145,共10页Data Analysis and Knowledge Discovery

基　　金：沈阳市科技项目基金项目(项目编号:20-202-1-28)的研究成果之一。

摘　　要：[目的]解决交互式检索场景中下位词扩展存在的主题漂移问题通过.[方法]利用图注意力网络编码概念链与文本关系图节点,其中,概念链通过词交互过程建模获得,关系图通过字共现关系获得.通过引入注意力机制,克服传统文本编码过程中丢失查询场景信息的问题.[结果]实验结果表明,本文方法比最好方法PRGC的F1值提升2.0%.[局限]本文方法针对交互式场景进行设计,对交互数据的质量存在一定依赖性.[结论]本文模型将概念链的结构特征和语义特征有效融合到文本特征中,同时对概念链和候选文本进行注意力计算,减少了在编码过程中造成的场景主题信息损失,缓解了主题漂移问题.[Objective]This study addresses the issue of topic drift in hyponym expansion in interactive retrieval scenarios.[Methods]We used a graph attention network to encode the relationship graph's nodes between concept chains and texts.Then,we modeled the concept chains through the word interaction processes and obtained the relationship graph based on character co-occurrence relations.By introducing the attention mechanism,our method overcomes the problem of losing query scenario information in traditional text encoding processes.[Results]The proposed method improved the F1 score by 2.0%compared to the best method,PRGC.[Limitations]The proposed method was designed for interactive scenarios and depended on the interactive data quality.[Conclusions]The proposed model effectively integrates the concept chains'structural and semantic features into text features.It also calculates attention for concept chains and candidate texts,reducing the loss of scenario topic information during encoding and mitigating the topic drift problem.

关 键 词：下位词扩展 概念链 图注意力网络 主题漂移 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]