(School of Chinese Language and Literature, Inner Mongolia Normal University, Hohhot 010022, China)
(College of Computer Science and Technology, Inner Mongolia MINZU University, Tongliao 028000, China)
(College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China)
(School of Computer Science and Technology, Hainan University, Haikou 570228, China)
[Objective] General NER has limitations in different research fields, hence specific domain NER for etiquette entity is necessary for structured organization of ancient ceremonial knowledge, facilitating in-depth exploration of cultural connotations of ancient Chinese ritual etiquette. [Methods] This paper introduces a multi-task deep learning approach for automatic recognition of diverse etiquette entities. 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 vehicle, food, 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 across all categories.
[Conclusions] The model constructed in this article 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.
