Learning to Identify Sentence Parallelism in Student Essays
Wei Song, Tong Liu, Ruiji Fu, Lizhen Liu, Hanshi Wang, Ting Liu
Abstract
Parallelism is an important rhetorical device. We propose a machine learning approach for automated sentence parallelism identification in student essays. We build an essay dataset with sentence level parallelism annotated. We derive features by combining generalized word alignment strategies and the alignment measures between word sequences. The experimental results show that sentence parallelism can be effectively identified with a F1 score of 82% at pair-wise level and 72% at parallelism chunk level. Based on this approach, we automatically identify sentence parallelism in more than 2000 student essays and study the correlation between the use of sentence parallelism and the types and quality of essays.- Anthology ID:
- C16-1076
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 794–803
- Language:
- URL:
- https://aclanthology.org/C16-1076
- DOI:
- Cite (ACL):
- Wei Song, Tong Liu, Ruiji Fu, Lizhen Liu, Hanshi Wang, and Ting Liu. 2016. Learning to Identify Sentence Parallelism in Student Essays. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 794–803, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Learning to Identify Sentence Parallelism in Student Essays (Song et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/C16-1076.pdf