صورة الغلاف

Chinese Mathematical Knowledge Entity Recognition Based on Linguistically Motivated Bidirectional Encoder Representation from Transformers

محفوظ في:
الحاوية / القاعدة:Information vol. 16, no. 1 (2025), p. 42
المؤلف الرئيسي: Song, Wei
مؤلفون آخرون: He, Zheng, Ma, Shuaiqi, Zhang, Mingze, Guo, Wei, Keqing Ning
منشور في:
MDPI AG
الموضوعات:
Language
Accuracy
Machine learning
Dictionaries
Deep learning
Recommender systems
Artificial intelligence
Conditional random fields
Information retrieval
Recognition
Artificial neural networks
Neural networks
Graphical representations
Knowledge representation
Semantics
Natural language
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مستخلص:We assessed whether constructing a mathematical knowledge graph for a knowledge question-answering system or a course recommendation system, Named Entity Recognition (NER), is indispensable. The accuracy of its recognition directly affects the actual performance of these subsequent tasks. In order to improve the accuracy of mathematical knowledge entity recognition and provide effective support for subsequent functionalities, this paper adopts the latest pre-trained language model, LERT, combined with a Bidirectional Gated Recurrent Unit (BiGRU), Iterated Dilated Convolutional Neural Networks (IDCNNs), and Conditional Random Fields (CRFs), to construct the LERT-BiGRU-IDCNN-CRF model. First, LERT provides context-related word vectors, and then the BiGRU captures both long-distance and short-distance information, the IDCNN retrieves local information, and finally the CRF is decoded to output the corresponding labels. Experimental results show that the accuracy of this model when recognizing mathematical concepts and theorem entities is 97.22%, the recall score is 97.47%, and the F1 score is 97.34%. This model can accurately recognize the required entities, and, through comparison, this method outperforms the current state-of-the-art entity recognition models.
تدمد:2078-2489
DOI:10.3390/info16010042
المصدر:Advanced Technologies & Aerospace Database