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- W4386870583 abstract "Event detection (ED) is a crucial area of natural language processing that automates the extraction of specific event types from large-scale text, and studying historical ED in classical Chinese texts helps preserve and inherit historical and cultural heritage by extracting valuable information. However, classical Chinese language characteristics, such as ambiguous word classes and complex semantics, have posed challenges and led to a lack of datasets and limited research on event schema construction. In addition, large-scale datasets in English and modern Chinese are not directly applicable to historical ED in classical Chinese. To address these issues, we constructed a logical event schema for classical Chinese historical texts and annotated the resulting dataset, which is called classical Chinese Historical Event Dataset (CHED). The main challenges in our work on classical Chinese historical ED are accurately identifying and classifying events within cultural and linguistic contexts and addressing ambiguity resulting from multiple meanings of words in historical texts. Therefore, we have developed a set of annotation guidelines and provided annotators with an objective reference translation. The average Kappa coefficient after multiple cross-validation is 68.49%, indicating high quality and consistency. We conducted various tasks and comparative experiments on established baseline models for historical ED in classical Chinese. The results showed that BERT+CRF had the best performance on sequence labeling task, with an f1-score of 76.10%, indicating potential for further improvement (The CHED data is released on https://github.com/lcclab-blcu/CHED )." @default.
- W4386870583 created "2023-09-20" @default.
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- W4386870583 date "2023-01-01" @default.
- W4386870583 modified "2023-09-27" @default.
- W4386870583 title "CHED: A Cross-Historical Dataset with a Logical Event Schema for Classical Chinese Event Detection" @default.
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- W4386870583 doi "https://doi.org/10.1007/978-981-99-6207-5_18" @default.
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