Method and device for denoising causal relation extraction based on multi-task collaborative learning

Abstract

Disclosed is a causal relation extraction method, specifically relating to a method and a device for denoising causal relation extraction based on multi-task collaborative learning. The method includes: inputting a text to be processed into a task-sharing layer of a denoising causal relation extraction model based on multi-task collaborative learning for feature extraction to obtain an extraction result; inputting the extraction result into the task-specific layer of the denoising causal relation extraction model based on multi-task collaborative learning to perform part-of-speech tagging, chunking analysis, and causal extraction tasks, to obtain feature labels corresponding to the tasks; performing causal strength classification and causal relation extraction tasks; conducting causal strength representation learning based on counterfactual negative sample contrastive learning, using a gating generator to obtain channel gains and temperature control coefficients, which are applied to gated attention calculation, to realize causal strength classification and guide extraction of causal relations through causal strength.