HTlinker: A head-to-tail linker for nested named entity recognition

Abstract

Named entity recognition (NER) aims to extract entities from unstructured text, and a nested structure often exists between entities. However, most previous studies paid more attention to flair named entity recognition while ignoring nested entities. The importance of words in the text should vary for different entity categories. In this paper, we propose a head-to-tail linker for nested NER. The proposed model exploits the extracted entity head as conditional information to locate the corresponding entity tails under different entity categories. This strategy takes part of the symmetric boundary information of the entity as a condition and effectively leverages the information from the text to improve the entity boundary recognition effectiveness. The proposed model considers the variability in the semantic correlation between tokens for different entity heads under different entity categories. To verify the effectiveness of the model, numerous experiments were implemented on three datasets: ACE2004, ACE2005, and GENIA, with F1-scores of 80.5%, 79.3%, and 76.4%, respectively. The experimental results show that our model is the most effective of all the methods used for comparison.