Named entity recognition method based on multi-granularity cognition

Abstract:

In the field of data scarcity, the performance of named entity recognition is limited by the expression of underfitting word features. The named entity recognition effect can be improved by introducing conventional multitask learning methods, but additional labeling costs are required. Aiming at addressing this problem, we propose a new named entity recognition method based on multigranularity cognition, which can enhance the character feature information and improve the performance of named entity recognition without incurring additional tagging costs. In order to optimize the expression of word embedding, in this approach, we start from the multi granularity cognition theory and use BiLSTM and CRF as the basic model, the task of named entity recognition under word granularity is combined with the task of entity number prediction under sentence global granularity. Multiple experiments on three different types of data sets show that the method of introducing multigranularity cognition method can effectively improve the performance of named entity recognition.