Classifying Ancient Chinese Text Relations with Entity Information
[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 Fl 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.
Ancient ChineseRelation ExtractionRelation ClassificationPre-trained Language ModelEntity Information
万方数据
维普
