A segment labeling method for Chinese nested named entity recognition
Tianci Shang, Baosong Deng · International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022) · 2022
Named entity recognition (NER) is one of the fundamental technologies in natural language processing. NER task extracts the entities such as location, person and organization from unstructured texts, which plays an important role in machine translation and other tasks. However, lacking of effective solutions of nested named entities to fuzzy boundary makes Chinese named entity recognition much harder than other languages. In order to solve the challenges brought by Chinese nested named entities, a segment labeling method for Chinese named nested entity recognition is proposed in this paper. Firstly, every entity is treated as several non-separable units to improve the labeling method. Secondly, each unit will be tagged differently according to its grammar and function in a particular sentence. Finally, every main entity in the recognition sequence is considered as a starting point, and merges each sub-entity into a complete nested entity after a candidate test. To verify the effectiveness of the method, a Chinese military corpus is collected to test the performance of this proposed method. The accuracy value, recall value and F1 value of this method are higher than those of the ordinary methods, which shows that this method can effectively improve the performance of Chinese named entity recognition.