Research on Chinese named entity recognition based on multi-feature fusion using transformer
Tianyi Liu, Runjie Liu · 2024
Named Entity Recognition (NER) is a critical task in Natural Language Processing (NLP). Compared to English NER, Chinese NER faces several issues due to differences in grammar between Chinese and English, which affect the performance of Chinese NER models. The current mainstream solution is to innovate lexical information into character-level models to achieve lexical enhancement. However, this process can introduce incorrect or irrelevant lexical information, leading to conflicts between words and affecting entity boundary segmentation and category annotation. To address this issue, this paper proposes a multi-feature fusion model based on Transformer. Building on the original model, which uses character vectors and word vectors as feature inputs, we add a new type of feature input: vectors obtained by re-weighting different words through adjusting lexical weights. This approach reduces the impact of incorrect lexical information, thereby enhancing model performance. Experiments on multiple datasets demonstrate the effectiveness of this method.