Abstract
Pleonasms are words that are redundant. To aid the development of systems that detect pleonasms in text, we introduce an annotated corpus of semantic pleonasms. We validate the integrity of the corpus with interannotator agreement analyses. We also compare it against alternative resources in terms of their effects on several automatic redundancy detection methods.- Anthology ID:
- N18-2036
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 225–230
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/N18-2036/
- DOI:
- 10.18653/v1/N18-2036
- Cite (ACL):
- Omid Kashefi, Andrew T. Lucas, and Rebecca Hwa. 2018. Semantic Pleonasm Detection. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 225–230, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Semantic Pleonasm Detection (Kashefi et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/N18-2036.pdf