A disambiguation method for potential ambiguities in Chinese based on knowledge graphs and large language model

IF 6.2 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Dan Zhang , Delong Jia
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引用次数: 0

Abstract

Traditional disambiguation methods struggle to effectively balance and integrate a wide range of contextual information and world knowledge when dealing with potential ambiguities in Chinese. To address this issue, this paper proposes a disambiguation model that integrates knowledge graphs and large language models (LLMs) to tackle lexical ambiguity in Chinese texts. This article uses an attention based disambiguation model, which is fine-tuned using multiple hyperparameter configurations. It optimizes network layers and knowledge graph embedding dimensions to enhance performance. Visualization of the attention mechanism reveals the model's focus on target words, context, and knowledge graph entities. Experiments conducted on a dataset comprising 200,000 sentences demonstrate significant improvements in accuracy and F1 scores, reaching 92.4 % and 91.9 %, respectively, compared to traditional statistical and deep learning models. Visualization of the attention mechanism reveals the model's focus on target words, context, and knowledge graph entities. The findings suggest that integrating knowledge graphs with LLMs offers an innovative approach to complex language tasks. In practical applications such as machine translation and chatbots, this model is expected to enhance both performance and interpretability.
基于知识图和大语言模型的汉语潜在歧义消歧方法
传统的消歧方法在处理汉语潜在歧义时,难以有效地平衡和整合广泛的语境信息和世界知识。为了解决这一问题,本文提出了一种集成知识图和大型语言模型(llm)的消歧模型来解决汉语文本中的词汇歧义。本文使用基于注意力的消歧模型,该模型使用多个超参数配置进行了微调。通过优化网络层和知识图嵌入维数来提高性能。注意机制的可视化显示了模型对目标词、上下文和知识图谱实体的关注。在包含20万个句子的数据集上进行的实验表明,与传统的统计和深度学习模型相比,准确率和F1分数分别达到了92.4 %和91.9 %。注意机制的可视化显示了模型对目标词、上下文和知识图谱实体的关注。研究结果表明,将知识图谱与法学硕士相结合,为复杂的语言任务提供了一种创新的方法。在机器翻译和聊天机器人等实际应用中,该模型有望提高性能和可解释性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
alexandria engineering journal
alexandria engineering journal Engineering-General Engineering
CiteScore
11.20
自引率
4.40%
发文量
1015
审稿时长
43 days
期刊介绍: Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification: • Mechanical, Production, Marine and Textile Engineering • Electrical Engineering, Computer Science and Nuclear Engineering • Civil and Architecture Engineering • Chemical Engineering and Applied Sciences • Environmental Engineering
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