Chinese nested entity recognition method for the finance domain based on heterogeneous graph network

In the finance domain, nested named entities recognition has become a hot topic in named entity recognition tasks. Traditional nested entity recognition methods easily ignore the dependency relationships between entities, and these methods are mostly suitable for English general domain. Therefore, we propose a Chinese nested entity recognition method for the finance domain based on heterogeneous graph network(HGFNER). This method consists of two parts: the boundary division model of candidate entities and the internal relationship graph model of candidate entities. First, the boundary division model of candidate entities that introduces expert knowledge is used to partition the flat entities contained in the text and segment the text to address issues such as long entity boundaries and strong domain features in the Chinese finance domain. Then, by using heterogeneous graphs to represent the internal structure of entities from both spatial and syntactic dependencies to achieve the goal of learning dependency relationships between entities from multiple perspectives. Meanwhile, so as not to affect the operational efficiency of the model, we also propose a fast matching algorithm DAAC_BM for n-gram sequences in domain dictionaries to solve the problems of memory overflow and space waste faced by multi-pattern fast matching algorithms in Chinese matching. In addition, we propose a Chinese nested entity dataset CFNE for the financial field, which, as far as we know, is the first publicly available annotated dataset in the field. HGFNER achieves state-of-the-art macro-F1 value on CFNE, reaching 86.41%.