Multi label text classification method based on label semantic matching fusion
In current research on multi label text classification,there are problems such as insufficient ex-traction of effective information from text,ignored correlation between labels,and insufficient mining and utilization of semantic attention from text to labels.This article proposes a multi label text classifica-tion method based on tag semantic matching fusion.Firstly,use the DeBERTa model to calculate fine-grained word level text representations;At the same time,label graph data is constructed based on global co-occurrence of labels,and graph attention networks are used to automatically learn the degree of association between different labels,generating label feature embeddings that capture the structural infor-mation and deep correlation between labels.Then,an embedding fusion mechanism based on label seman-tic matching was proposed to model the semantic attention of text to labels,reflecting the semantic correla-tion between the two.The obtained word fusion representation based on label semantic matching embedding was fed into CNN for feature interaction,ultimately achieving label prediction.The experimental results on two publicly available English datasets,AAPD and RCV1-V2,show that the performance of the pro-posed model is significantly superior to other mainstream baseline models.
multi label text classificationDeBERTagraph attention networkslabel semantic embedding
NETL
NSTL
万方数据
