Multidimensional Feature Named Entity Recognition Method in Education Domain
The development and progress of information technology have resulted in extensive investigations into"Internet+Education"in the field of education,and all aspects of education and teaching are being developed in the direction of intelligence.The study of Named Entity Recognition(NER)in secondary school mathematics can provide a foundation for the subsequent construction of secondary school mathematics knowledge mapping and automatic question-and-answer tasks to fulfill the demands of secondary school students for personalized knowledge acquisition and facilitate the construction of a new intelligent education system.Currently,owing to the semantic complexity of secondary school mathematics knowledge,its NER and research data are insufficient,and the current mainstream model for feature extraction disregards some local features.To solve the challenges of entity recognition in this field,a multidimensional feature NER method incorporating multihead attention is proposed using a self-constructed corpus of secondary school mathematics knowledge.First,the method adopts Bidirectional Encoder Representations from Transformers(BERT)for pre-training text representations to obtain word vectors.Subsequently,this method introduces adversarial training to perturb each embedding vector and then transmits the obtained adversarial samples and embedding vectors to the multidimensional feature extraction layer for feature extraction.Next,it splices the output features,dynamically fuses them via the multihead attention mechanism,and finally outputs them after correction by a Conditional Random Field(CRF).Experimental results show that the accuracy,recall,and F1 value of this method for recognizing the self-constructed Educ dataset are 96.68％,97.71％,and 97.19％,respectively,thus demonstrating its effectiveness in recognizing mathematical knowledge entities in secondary schools.
Named Entity Recognition(NER)educational domainadversarial trainingmultidimensional feature extractionmulti-head attention mechanism
国家科技期刊平台
NSTL
万方数据
