Abstract
In this paper, we construct a Chinese literary grace corpus, CLGC, with 10,000 texts and more than 1.85 million tokens. Multi-level annotations are provided for each text in our corpus, including literary grace level, sentence category, and figure-of-speech type. Based on the corpus, we dig deep into the correlation between fine-grained features (semantic information, part-of-speech and figure-of-speech, etc.) and literary grace level. We also propose a new Literary Grace Evaluation (LGE) task, which aims at making a comprehensive assessment of the literary grace level according to the text. In the end, we build some classification models with machine learning algorithms (such as SVM, TextCNN) to prove the effectiveness of our features and corpus for LGE. The results of our preliminary classification experiments have achieved 79.71% on the weighted average F1-score.- Anthology ID:
- 2022.lrec-1.594
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5548–5556
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.594
- DOI:
- Cite (ACL):
- Yi Li, Dong Yu, and Pengyuan Liu. 2022. CLGC: A Corpus for Chinese Literary Grace Evaluation. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5548–5556, Marseille, France. European Language Resources Association.
- Cite (Informal):
- CLGC: A Corpus for Chinese Literary Grace Evaluation (Li et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.lrec-1.594.pdf
- Code
- blcunlp/clgc