Intelligent Error Detection in Electric Power Texts Based on ALBERT-GCNN-CRF Model

Detecting grammatical errors in electric power texts is a crucial task in natural language processing, aimed at identifying typos, grammatical issues, and sentence structure problems. Due to the flexibility of grammar and the absence of features such as tense and voice, local information is essential. Traditional machine learning methods often struggle with accuracy, and existing deep learning methods fail to fully leverage local information. We propose a novel model, ALBert-GCNN-CRF, which combines the ALBert pre-trained language model with a convolutional neural network (GCNN) for local information extraction and a residual gating mechanism. This model treats grammatical error detection as a sequence labeling task, using a Conditional Random Field (CRF) to learn relationships between labels. Experimental results show that the ALBert-GCNN-CRF model significantly improves the F1 score for electric power texts, with average increases of 16.9%, 11.2%, and 4.4% compared to other models, demonstrating its effectiveness in enhancing grammatical error detection.