ERPG: Enhancing Entity Representations with Prompt Guidance for Complex Named Entity Recognition

Recently, sequence generation methods are widely used in complex named entity recognition. By selecting high-related tokens to generate complex named entities, these methods obtain several achievements. However, due to lack of guidance in learning output format and ignoring labels in obtaining features, sequence generation methods suffer invalid output and inaccurate recognition. To solve that, we propose an Enhancing Entity Representation method with Prompt Guidance (ERPG). Specifically, in order to reduce invalid output, we design the candidate entity generation module that generate candidate entities and their labels as expected. Besides, to accurately recognize candidate entities, we propose candidate entity refine module, which obtain distinguishable candidate entity representations and filter them accurately. Based on that, our method finally outperforms baselines by 1.20, 1.62 and 0.69 F1 scores in ACE2004, GENIA and CADEC corpora, which proves the effectiveness in complex named entity recognition.