Few-Shot Named Entity Recognition with the Integration of Spatial Features  

作　　者：LIU Zhiwei HUANG Bo XIA Chunming XIONG Yujie ZANG Zhensen ZHANG Yongqiang 

机构地区：[1]College of Electrical and Electronic Engineering,Shanghai University of Engineering Science,Shanghai 201620,China [2]Shanghai Zhongyu Academy of Industrial Internet,Shanghai 201620,China [3]AIoT Manufacturing Solutions Technology Co.,Ltd.,Hefei 230000,Anhui,China

出　　处：《Wuhan University Journal of Natural Sciences》2024年第2期125-133,共9页武汉大学学报（自然科学英文版）

基　　金：Supported by the Scientific and Technological Innovation 2030-Major Project of New Generation Artificial Intelligence(2020AAA0109300);Science and Technology Commission of Shanghai Municipality(21DZ2203100);2023 Anhui Province Key Research and Development Plan Project-Special Project of Science and Technology Cooperation(2023i11020002)。

摘　　要：The few-shot named entity recognition(NER)task aims to train a robust model in the source domain and transfer it to the target domain with very few annotated data.Currently,some approaches rely on the prototypical network for NER.However,these approaches often overlook the spatial relations in the span boundary matrix because entity words tend to depend more on adjacent words.We propose using a multidimensional convolution module to address this limitation to capture short-distance spatial dependencies.Additionally,we uti-lize an improved prototypical network and assign different weights to different samples that belong to the same class,thereby enhancing the performance of the few-shot NER task.Further experimental analysis demonstrates that our approach has significantly improved over baseline models across multiple datasets.

关 键 词：named entity recognition prototypical network spatial relation multidimensional convolution 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]