Abstract
Morphological analysis is an important research issue in the field of natural language processing. In this study, we propose a context-free morphological analysis task, namely word-level prefix/suffix sense detection, which deals with the ambiguity of sense expressed by prefix/suffix. To research this novel task, we first annotate a corpus with prefixes/suffixes expressing negation (e.g., il-, un-, -less) and then propose a novel few-shot learning approach that applies an input-augmentation prompt to a token-replaced detection pre-training model. Empirical studies demonstrate the effectiveness of the proposed approach to word-level prefix/suffix negation sense detection.- Anthology ID:
- 2023.findings-acl.484
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7651–7658
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.484
- DOI:
- 10.18653/v1/2023.findings-acl.484
- Cite (ACL):
- Yameng Li, Zicheng Li, Ying Chen, and Shoushan Li. 2023. Word-level Prefix/Suffix Sense Detection: A Case Study on Negation Sense with Few-shot Learning. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7651–7658, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Word-level Prefix/Suffix Sense Detection: A Case Study on Negation Sense with Few-shot Learning (Li et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.findings-acl.484.pdf