Automatic Essay Scoring Method Based on Topic Perception and Semantic Enhancement
Automatic Essay Scoring(AES)is an important research topic for the application of Natural Language Processing(NLP)technology in the field of education.AES aims to improve scoring efficiency and enhance the objectivity and reliability of evaluations.This study proposes a topic perception and semantic enhancement approach for AES,addressing the issues of missing thematic relevance and loss of information in long texts,as well as leveraging the different levels of feature extraction capability in the pre-training language model,Bidirectional Encoder Representations from Transformers(BERT).This approach utilizes a multi-head attention mechanism to extract shallow semantic features of an essay and perceive its thematic characteristics.Additionally,it leverages the mid-level syntactic and deep semantic features of BERT to enhance the understanding of the semantics of the essay.Finally,the fused features from different dimensions are used for the AES.Experimental results indicate that the proposed model exhibits significant performance advantages for eight subsets of the ASAP public dataset.The proposed model effectively improves the performance of AES compared to that of baseline models,such as Qwen-7B;its average Quadratic Weighted Kappa(QWK)is 80.25％.
Automatic Essay Scoring(AES)semantic enhancementtopic perceptionfeature fusionpre-training language model
万方数据
维普
