Traditional methods of document-level relation extraction have limitations in the effectiveness of feature representation and noise elimination.To address this issue,this paper proposes a method that utilizes wavelet trans-form to extract,clean,and denoise text vectors generated by pre-trained language models.Firstly,the document is encoded by a pre-trained language model,and the obtained initial text vectors are applied to wavelet transform to ob-tain more precise features.Next,a multi-head attention mechanism is introduced to weight the data from wavelet transform,highlighting the important features relevant to entity relationships.To fully utilize both original and cleaned data,a residual connection is employed to fuse them together.Experiment on the DocRED dataset demon-strate that the proposed method performs better in extracting relationships between entity pairs.
维普
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