Few-shot relation extraction based on multi-level feature metric learning

YaXin Fang; ZhongLin Liu; Shuang Pan; Qian Li

doi:10.1117/12.2671059

28 April 2023 Few-shot relation extraction based on multi-level feature metric learning

YaXin Fang, ZhongLin Liu, Shuang Pan, Qian Li

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 1261058 (2023) https://doi.org/10.1117/12.2671059
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Relationship extraction is an important task in natural language processing and knowledge mapping. Traditional entity relationship extraction methods have achieved high accuracy in practical applications. However, when faced with the task of entity relationship extraction that is not easy to obtain large-scale supervision training data sets, traditional methods cannot get good results. In this paper, a few-shot relation extraction method based on multi-level feature metric learning is proposed. This method takes the prototype network as the baseline network to generate a class prototype. Firstly, a multi-level feature extraction module is proposed. This module combines the multi-level features of the text with the multi-level attention mechanism, which can fully extract the features of the text. Secondly, a loss function based on label value and negative sample distance is proposed. This algorithm introduces an evaluation mechanism of negative sample distance on the basis of the prototype network, so that the model can adaptively allocate parameters and improve the clustering ability of small samples. Experiments are conducted on FewRel1.0 which is a small sample relational data set. Experiment results show that compared with other models, our model can improve classification accuracy.

Citation Download Citation

YaXin Fang, ZhongLin Liu, Shuang Pan, and Qian Li "Few-shot relation extraction based on multi-level feature metric learning", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 1261058 (28 April 2023); https://doi.org/10.1117/12.2671059

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KEYWORDS

Feature extraction

Education and training

Prototyping

Semantics

Convolution

Matrices

Transformers

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