Multi-Task Learning for Ancient Ritual Literature Etiquette Entity Recognition
[Objective]This paper proposes a multi-task deep learning model tailored for ancient texts to overcome the limitations of current NER models,enhancing the identification of complex etiquette entity with improved accuracy and efficiency.[Methods]We built a named entity annotated corpus with six categories and employed a combined model,MJL-SikuRoBERTa-BiGRU-CRF.SikuRoBERTa and BiGRU extract contextual semantic information,while CRF imposes label constraints on both tasks,generating globally optimal named entity and punctuation label sequences.[Results]The proposed model has an F1 value of 84.34％on the etiquette recognition task and an F1 value of 75.30％on the automatic punctuation task.Among them,the palace,utensils,and costume moniker categories are effective with an F1 value of more than 85％,while the food,vehicle,and products categories are slightly underperformed with an F1 value of 76％～81％.[Limitations]The model did not validate finer-grained named entity classification,and the paper attempted to augment named entity recognition for cultural entities,but not for all categories.[Conclusions]The model constructed in this paper is more suitable for named entity recognition tasks in classical Chinese ritual texts and can effectively support information extraction and knowledge graph construction related to ancient rituals.
Etiquette Entity RecognitionAncient Ritual LiteratureMulti-Task LearningPretrained Model for Classical Chinese Language
NETL
NSTL
万方数据
