Deep learning based high similarity automatic retrieval algorithm for vocabulary interpretation of workers of Food Sector in china

Xuezhong Wu
Zhejiang Yuexiu University of Foreign Languages, Shaoxing, 312030, China
Cong Wu
Shangyu Senior Middle School, Shaoxing, 312300, China

DOI:https://doi.org/10.5912/jcb1249

Abstract:

In order to build a high similarity English vocabulary interpretation domain knowledge base and ensure the automatic retrieval effect of high similarity English vocabulary interpretation, this paper standardizes the automatic retrieval specification of authoritative interpretation of high similarity English vocabulary knowledge, and takes high similarity English vocabulary as the source corpus of the knowledge base. On the basis of the existing work, this paper attempts to propose an automatic retrieval algorithm of high similarity English word interpretation based on deep learning. The goal is to diversify the sources of high similarity English word knowledge and achieve the accuracy of automatic retrieval of word interpretation while ensuring a certain knowledge coverage. A suitable domain knowledge base of machine-readable dictionary is constructed through a new method It can not only provide accurate knowledge information for high similarity English vocabulary, but also provide retrieval verification for user needs analysis and high similarity English vocabulary indexing of snippet. The experimental results show that the algorithm based on deep learning is effective and can fully meet the research requirements.