Semi-Supervised New Intention Discovery for Syntactic Elimination and Fusion in Elastic Neighborhoods  

作　　者：Di Wu Liming Feng Xiaoyu Wang 

机构地区：[1]School of Information and Electronic Engineering,Hebei University of Engineering,Handan,056038,China

出　　处：《Computers, Materials & Continua》2025年第4期977-999,共23页计算机、材料和连续体(英文)

基　　金：supported by Research Projects of the Nature Science Foundation of Hebei Province(F2021402005).

摘　　要：Semi-supervised new intent discovery is a significant research focus in natural language understanding.To address the limitations of current semi-supervised training data and the underutilization of implicit information,a Semi-supervised New Intent Discovery for Elastic Neighborhood Syntactic Elimination and Fusion model(SNID-ENSEF)is proposed.Syntactic elimination contrast learning leverages verb-dominant syntactic features,systematically replacing specific words to enhance data diversity.The radius of the positive sample neighborhood is elastically adjusted to eliminate invalid samples and improve training efficiency.A neighborhood sample fusion strategy,based on sample distribution patterns,dynamically adjusts neighborhood size and fuses sample vectors to reduce noise and improve implicit information utilization and discovery accuracy.Experimental results show that SNID-ENSEF achieves average improvements of 0.88%,1.27%,and 1.30%in Normalized Mutual Information(NMI),Accuracy(ACC),and Adjusted Rand Index(ARI),respectively,outperforming PTJN,DPN,MTP-CLNN,and DWG models on the Banking77,StackOverflow,and Clinc150 datasets.The code is available at https://github.com/qsdesz/SNID-ENSEF,accessed on 16 January 2025.

关 键 词：Natural language understanding semi-supervised new intent discovery syntactic elimination contrast learning neighborhood sample fusion strategies bidirectional encoder representations from transformers(BERT) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]