Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6A): 240400139-7.doi: 10.11896/jsjkx.240400139

• Large Language Model Technology and Its Application • Previous Articles     Next Articles

Research on Semantic Fusion of Chinese Polysemous Words Based on Large LanguageModel

YIN Baosheng, ZONG Chen   

  1. Human-Machine Intelligence Research Center,Shenyang Aerospace University,Shenyang 110136,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:YIN Baosheng,born in 1975,professor,is a member of CCF(No.Q7935M).His main research interests include deep learning and natural language proces-sing.
    ZONG Chen,born in 1997,graduate student.His main research interests include natural language processing and word sense disambiguation.
  • Supported by:
    Project of Liaoning Provincial Department of Education(LJKMZ20220536).

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Abstract: Aiming at the polysemy characteristics of Chinese words,it is of great significance to construct a comprehensive and standardized Chinese polysemy knowledge base based on existing Chinese dictionary resources for Chinese semantic analysis,intelligent question answering,machine translation,and the optimization and evaluation of the disambiguation ability of large language models.This paper makes an in-depth analysis of the problems such as “the same meanings but different descriptions” in the integration of Modern Chinese Dictionary and Modern Chinese Standard Dictionary and other resources.Furthermore,it innovatively proposes the polysemical meaning fusion technology based on large language model and prompt learning,which fully uses the large language model’s ability to analyze and understand common sense knowledge and assist decision-making.The automatic fusion of polysemous terms is accomplished by means of effective problem decomposition strategy,prompt template design and cross-validation of semantic relation.Experimental results show that on the evaluation data of 50 polysemous words with a total of 754 sense pairs extracted by normal distribution,the accuracy of sense fusion based on the above algorithm is 96.26%,and the Dice coefficient is 0.973 3.This study verifies the feasibility and effectiveness of using the large language model to carry out automatic processing of Chinese knowledge resources.Compared with the traditional processing mode relying on language experts,it significantly improves the efficiency of knowledge processing on the premise of ensuring higher quality.

Key words: Polysemous word, Semantic fusion, Large language model, Cue learning, Chinese information processing

CLC Number: 

  • TP391.1
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