古汉语实体关系联合抽取的标注方法

Annotation Method for Extracting Entity Relationship from Ancient Chinese Works

【目的】针对古汉语数据集标注规范研究缺失的现实,提出一套面向古汉语的实体关系标注方法。【方法】通过对逻辑语义学、深度学习、历史学的有机融合,提出古汉语实体关系抽取数据集标注方法,由"关系配价标注""命题逻辑标注"以及"单一关系存在"原则构成,适用于小样本学习。【结果】利用Word Embedding-BiGRU-CRF端到端关系序列标注模型,在《史记》文本数据集上进行实验,在实体关系抽取与命题逻辑抽取任务上F1值分别达到42.02%与34.07%。【局限】未使用BERT、ALBERT等预训练模型,而是选择了较为经典的Word2Vec模型完成词嵌入任务。从模型最终的结果来看,相关研究仍有较大的上升空间。【结论】初步验证了标注方法与联合抽取模型的可行性,填补了面向古汉语实体关系抽取的研究空白。

[Objective]This paper proposes an annotation method for ancient Chinese datasets,aiming to standardize the annotation procedures.[Methods]We proposed a new method integrating logical semantics,deep learning and history knowledge.This model,which is suitable for few-shot learning,includes three principles of"annotation of relationship valence","annotation of propositional logic","existence of a single relationship".[Results]We examined the proposed annotation model with the text dataset of Shiji(Historical Records in Chinese),and found its F1 values for the tasks of relationship extraction and the propositional logic extraction reached 42.02%and 34.07%respectively.[Limitations]The proposed method,which did not include the pretrained models like BERT or ALBERT,only used the classic Word2Vec model for word embedding.The model’s performance could be further improved.[Conclusions]Our new annotation method could effectively extract entity relationship from Ancient Chinese works.