Joint Models for Sentence Segmentation and Named Entity Recognition in Literary Sinitic Text
DOI:
https://doi.org/10.13052/jwe1540-9589.2512Keywords:
Literary sinitic, sentence segmentation, NER, transformerAbstract
It is challenging to understand Literary Sinitic text from the Joseon dynasty, since there is a lack of explicit word separators, which creates significant semantic ambiguity. To address this, both sentence segmentation and named entity recognition (NER) are essential. We propose a Transformer-based analyzer that performs these two tasks simultaneously. Trained on a labeled corpus from the Seungjeongwon Ilgi, our model effectively segments sentences and identifies named entities, thereby significantly improving the understanding of sentence structure and overall context.
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