An aspect-level sentiment triple extraction method based on a diffusion model

By employing a boundary denoising diffusion process based on a diffusion model and a contrastive denoising training strategy, the problem of word generation limitations in generative models is solved, achieving efficient and accurate prediction of multi-word aspect/opinion terms and improving the performance of aspect-level sentiment triple extraction.

CN119168044BActive Publication Date: 2026-06-26UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Application Number
CN202411194940.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-08-28
Publication Date
2026-06-26
Estimated Expiration
2044-08-28

AI Technical Summary

Technical Problem

Existing generative models focus only on generating individual words during autoregressive decoding, limiting their ability to utilize holistic semantics when dealing with multi-word aspects/opinion terms.

Method used

We adopt a non-autoregressive diffusion model framework, defining the aspect-level sentiment triple extraction task as a boundary denoising diffusion process. By introducing Gaussian noise to simulate boundary uncertainty and introducing a contrastive denoising training strategy, we directly model the boundary index and use comprehensive contextual information for prediction.

Benefits of technology

It significantly improves the accuracy of aspect and opinion term boundary prediction, enhances the robustness and predictive performance of the model, and reduces the generation of erroneous triples.

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Abstract

The application relates to the technical field of sentiment analysis, and provides an aspect-level sentiment triple extraction method based on a diffusion model. The method is aimed at solving the problem that in the existing generative model, only a single word is focused on during autoregressive decoding, and the ability of the model to utilize overall semantics when processing multi-word aspect / opinion terms is limited. The method defines the aspect-level sentiment triple task as a boundary diffusion denoising process based on a non-autoregressive diffusion model framework, directly models the boundary index, gradually refines the boundary according to comprehensive context information, dynamically adjusts the boundary in the noise state, and further introduces a contrastive denoising training strategy, effectively alleviates the repeated prediction with subtle changes introduced by the diffusion process, and improves the aspect-level sentiment triple extraction performance. The method is used for sentiment analysis, especially when processing user sentiment expressions on social media and online review platforms, can more carefully mine sentiment information in the text, and provides a reference for product optimization and enterprise decision-making.
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