Chinese Named Entity Recognition method based on multi-feature fusion and biaffine

Abstract Chinese Named Entity Recognition (CNER) focuses on precisely identifying predefined structural categories in unstructured Chinese text. Most existing CNER models do not consider the unique glyph and pinyin features of Chinese characters, but the rich semantic features hidden behind these features have a good effect on enhancing the judgment ability of language models. At the same time, it is difficult to identify the boundaries of Chinese nested entities, and accurately identifying the boundaries of entities within nested entities is also a difficult problem to solve. We propose a CNER method based on multi-feature fusion technology and biaffine mechanism to address the above issues: In the input representation layer, integrate the glyph and pinyin features of Chinese characters together, intuitively capturing the semantics of Chinese characters. Furthermore, biaffine mechanism has been introduced to provide a comprehensive view of the input on a global scale. This mechanism effectively converts the task of entity recognition into a problem of assigning scores to spans, hence enhancing the precision of identifying entity borders. In order to evaluate the efficacy of the model, a series of experiments were done on three Chinese entity recognition datasets: Resume, MSRA, and People Daily. The experimental results show that the solid boundary can be identified more accurately, and the F1 values of 96.49%, 96.26% and 96.19% are obtained respectively, which has a better recognition effect than the baseline model.