Classifying Ancient Chinese Text Relations with Entity Information
Tang Xuemei1,2,Su Qi2,3(),Wang Jun1,2
1Department of Information Management, Peking University, Beijing 100871, China 2Center for Digital Humanities, Peking University, Beijing 100871, China 3School of Foreign Languages, Peking University, Beijing 100871, China
[Objective] This paper integrates entity information with pre-trained language models, which help us classify ancient Chinese relations. [Methods] Firstly, we utilized special tokens in the input layer of the pre-trained model to mark the positions of entity pairs. We also appended entity-type descriptions following the original relation sentences. Secondly, we extracted semantic information of entities from the output of the pre-trained language model. Thirdly, we employed a CNN model to incorporate positional information of each token relative to the start and end entities into the model. Finally, we concatenated sentence representations, entity semantic representations, and CNN outputs and passed them through a classifier to obtain relation labels. [Results] Compared to pre-trained language models, our new model’s Macro F1 score was 3.5% higher on average. [Limitations] Analysis of the confusion matrix reveals a tendency for errors in predicting relations with the same entity type pairs. [Conclusions] Combining entity information and pre-trained language models enhances the effectiveness of ancient Chinese relation classification.
唐雪梅, 苏祺, 王军. 融合实体信息的古汉语关系分类研究*[J]. 数据分析与知识发现, 2024, 8(1): 114-124.
Tang Xuemei, Su Qi, Wang Jun. Classifying Ancient Chinese Text Relations with Entity Information. Data Analysis and Knowledge Discovery, 2024, 8(1): 114-124.
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