@inproceedings{wang-etal-2024-sentence,
title = "Sentence Segmentation and Punctuation for Ancient Books Based on Supervised In-context Training",
author = "Wang, Shiquan and
Fu, Weiwei and
Li, Mengxiang and
He, Zhongjiang and
Li, Yongxiang and
Fang, Ruiyu and
Guan, Li and
Song, Shuangyong",
editor = "Sprugnoli, Rachele and
Passarotti, Marco",
booktitle = "Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lt4hala-1.31",
pages = "251--255",
abstract = "This paper describes the participation of team {``}TeleAI{''} in the third International Chinese Ancient Chinese Language Information Processing Evaluation (EvalHan24). The competition comprises a joint task of sentence segmentation and punctuation, categorized into open and closed tracks based on the models and data used. In the final evaluation, our system achieved significantly better results than the baseline. Specifically, in the closed-track sentence segmentation task, we obtained an F1 score of 0.8885, while in the sentence punctuation task, we achieved an F1 score of 0.7129.",
}
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<abstract>This paper describes the participation of team “TeleAI” in the third International Chinese Ancient Chinese Language Information Processing Evaluation (EvalHan24). The competition comprises a joint task of sentence segmentation and punctuation, categorized into open and closed tracks based on the models and data used. In the final evaluation, our system achieved significantly better results than the baseline. Specifically, in the closed-track sentence segmentation task, we obtained an F1 score of 0.8885, while in the sentence punctuation task, we achieved an F1 score of 0.7129.</abstract>
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%0 Conference Proceedings
%T Sentence Segmentation and Punctuation for Ancient Books Based on Supervised In-context Training
%A Wang, Shiquan
%A Fu, Weiwei
%A Li, Mengxiang
%A He, Zhongjiang
%A Li, Yongxiang
%A Fang, Ruiyu
%A Guan, Li
%A Song, Shuangyong
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F wang-etal-2024-sentence
%X This paper describes the participation of team “TeleAI” in the third International Chinese Ancient Chinese Language Information Processing Evaluation (EvalHan24). The competition comprises a joint task of sentence segmentation and punctuation, categorized into open and closed tracks based on the models and data used. In the final evaluation, our system achieved significantly better results than the baseline. Specifically, in the closed-track sentence segmentation task, we obtained an F1 score of 0.8885, while in the sentence punctuation task, we achieved an F1 score of 0.7129.
%U https://aclanthology.org/2024.lt4hala-1.31
%P 251-255
Markdown (Informal)
[Sentence Segmentation and Punctuation for Ancient Books Based on Supervised In-context Training](https://aclanthology.org/2024.lt4hala-1.31) (Wang et al., LT4HALA-WS 2024)
ACL
- Shiquan Wang, Weiwei Fu, Mengxiang Li, Zhongjiang He, Yongxiang Li, Ruiyu Fang, Li Guan, and Shuangyong Song. 2024. Sentence Segmentation and Punctuation for Ancient Books Based on Supervised In-context Training. In Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024, pages 251–255, Torino, Italia. ELRA and ICCL.