Chinese Named Entity Disambiguation Based on Multivariate Similarity Fusion
[Objective]This paper aims to solve the ambiguity problems arising from mapping multiple entities of the same name with different meanings to a knowledge base.It improves the accuracy of entity disambiguation.[Methods]We proposed a multi-dimensional similarity fusion method.It utilizes the semantic similarity of entity context,the entity attributes'background similarity,and the topic words'semantic similarity to characterize entities.[Results]We examined the new model on the agricultural dataset from Wikipedia.The proposed method achieved an accuracy of 89.7％,outperforming traditional methods.[Limitations]The proposed method is only applicable in specific fields.[Conclusions]The new method addresses the entity disambiguation issues in specific fields.It can be applied to a broader range of entity disambiguation scenarios.
Entity DisambiguationSimilarityContextual Word VectorEntity PropertiesTopic Word Vector
NETL
NSTL
万方数据
