Towards Hierarchical Categorical Named Entity Recognition via Injective Multi-task Learning Strategy

The Named Entity Recognition (NER) task aims to locate meaningful entities in a text and classify them into pre-defined categories. NER is a fundamental task in the field of natural language processing. In practice, entities often have multiple types at different levels of granularity, and it is essential to identify various levels of categories of entities simultaneously. A deep learning-based approach treats NER as a sequence annotation problem by giving each character a unique entity label, which significantly improves the accuracy of NER. Deep learning-based NER performs well on single-category and coarse-grained NER but does not perform well on hierarchical category NER problems. This paper proposes a multi-task learning-based hierarchical category named entity recognition model to solve this problem. The model uses a multi-task learning mechanism to simultaneously learn multiple levels of information of named entity recognition tasks. Named entity recognition results at higher levels are directly constructed from lower-level recognition structures. Experiments show that both systems can significantly improve the recognition performance at each level compared to the basic multi-tasking model. The model is also compared with each level's traditional named entity recognition method. The hierarchical category named entity recognition model can sig-nificantly improve the performance of named entity recognition tasks at each level.