A Chinese short text entity linking approach based on data enhancement and multidimensional feature fusion

Recently, with the rapid growth of the Internet, a considerable amount of textual data has emerged, injecting vitality into natural language processing. As a result of the diverse characteristics of natural language descriptions, humans and computers often face ambiguity problems when processing natural language text, and they are unable to combine context and prior knowledge to deeply understand the deep meaning of the text, which limits further intelligent processing of textual data. Entity linking, as a basic task, aims to link the entities mentioned in the textual data to the corresponding entities in the knowledge base. However, short text entity links face difficulties such as limited contextual information, irregular expressions, and incomplete grammatical structures. Furthermore, the flexibility and syntactic diversity of the Chinese language increase the challenge of understanding short Chinese texts on a deeper level. This paper puts forward a Chinese short text entity linking approach via data enhancement and multi-dimensional feature fusion. Experimental results demonstrate that our approach overtakes the baseline model on the Chinese short text entity linking dataset, indicating that our proposed model exhibits better competence and adaptability in handling Chinese short text entity linking tasks.