MP-NER: Morpho-Phonological Integration Embedding for Chinese Named Entity Recognition

Named Entity Recognition (NER) aims to automatically extract specific entities from unstructured text. Compared with English NER, Chinese NER faces challenges due to heterophony, where the same Chinese character may have different pronunciations and meanings. Additionally, the lack of clear separators between Chinese characters exacerbates these challenges, leading to difficulties in boundary detection and entity category determination. Inspired by the hieroglyphic and phonetic features of Chinese characters, this study proposes a multi-feature fusion embedding model (MP-NER). The model employs CNN for extracting radicals and phonetic features of Chinese characters, combines the encoded information from these features with pre-trained word vectors to generate fusion embedding vectors, and uses a fully-connected layer for feature transformation. Experiments were conducted on the Chinese benchmark datasets Resume, Weibo and MSRA. Compared to current mainstream models, the proposed model demonstrates superior performance in terms of F1 score, F1 score stability, and individual entity recognition accuracy. Ablation experiments further validate the effectiveness of the introduced radicals and phonetic features. The experimental results demonstrate that this model effectively captures the semantic information of Chinese characters, addresses the problem of Chinese character heterophony, and improves entity recognition performance. The code and datasets available at: https://github.com/FAKLITS/MP-NER