Predicting the Focus of Negation: Model and Error Analysis
Md Mosharaf Hossain, Kathleen Hamilton, Alexis Palmer, Eduardo Blanco
Abstract
The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.- Anthology ID:
- 2020.acl-main.743
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8389–8401
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.743
- DOI:
- 10.18653/v1/2020.acl-main.743
- Cite (ACL):
- Md Mosharaf Hossain, Kathleen Hamilton, Alexis Palmer, and Eduardo Blanco. 2020. Predicting the Focus of Negation: Model and Error Analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8389–8401, Online. Association for Computational Linguistics.
- Cite (Informal):
- Predicting the Focus of Negation: Model and Error Analysis (Hossain et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.acl-main.743.pdf