基于知识增强的文本语义匹配模型研究
A Text Semantic Matching Model Based on Knowledge Enhancement
张贞港 1余传明1
作者信息
- 1. 中南财经政法大学信息与安全工程学院,武汉 430073
- 折叠
摘要
文本语义匹配模型在信息检索、文本挖掘等领域已经获得了广泛应用.为解决现有模型主要从文本自身角度判断文本之间的语义关系而忽略对外部知识有效利用的问题,本文提出一种新的基于知识增强的文本语义匹配模型,以知识图谱实体作为外部知识,有效建模文本的外部知识信息,并自适应地过滤外部知识中存在的噪声.针对自然语言推理和释义识别两个文本语义匹配任务,与基线方法相比,本文模型在大多数指标上取得了最优效果.研究结果表明,本文模型有助于揭示知识图谱在文本语义匹配任务中的作用,为将知识图谱应用到智能信息服务领域提供了参考.
Abstract
Text semantic matching models have been widely used in information retrieval,text mining,and other fields.Current methods mainly predict the semantic relationship of text pairs from the perspective of the text itself,ignoring external knowledge.We propose a new text semantic matching model based on knowledge enhancement to address this issue.The model utilizes knowledge graph entities as external knowledge,effectively models the text's external knowledge information,and adaptively filters the noise in the external knowledge.Our model achieves the best results for most indicators in Natural Language Inference and Paraphrase Identification.This research will aid in the application of knowledge graphs in text semantic matching tasks and provide a reference for applying knowledge graphs to the information field.
关键词
文本语义匹配/信息检索/知识图谱/知识增强Key words
text semantic matching/information retrieval/knowledge graph/knowledge enhancement引用本文复制引用
基金项目
国家自然科学基金面上项目(72374219)
&&(71974202)
国家自然科学基金创新研究群体项目(71921002)
出版年
2024
NETL
NSTL
维普
万方数据