Abstract
This paper presents a method for applying automatically acquired knowledge to semantic role labeling (SRL). We use a large amount of automatically extracted knowledge to improve the performance of SRL. We present two varieties of knowledge, which we call surface case frames and deep case frames. Although the surface case frames are compiled from syntactic parses and can be used as rich syntactic knowledge, they have limited capability for resolving semantic ambiguity. To compensate the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles. We also consider quality management for both types of knowledge in order to get rid of the noise brought from the automatic analyses. The experimental results show that Chinese SRL can be improved using automatically acquired knowledge and the quality management shows a positive effect on this task.- Anthology ID:
- E17-1054
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 568–577
- Language:
- URL:
- https://aclanthology.org/E17-1054
- DOI:
- Cite (ACL):
- Gongye Jin, Daisuke Kawahara, and Sadao Kurohashi. 2017. Improving Chinese Semantic Role Labeling using High-quality Surface and Deep Case Frames. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 568–577, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Improving Chinese Semantic Role Labeling using High-quality Surface and Deep Case Frames (Jin et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/E17-1054.pdf