Abstract
Proper recognition and interpretation of negation signals in text or communication is crucial for any form of full natural language understanding. It is also essential for computational approaches to natural language processing. In this study we focus on negation detection in Dutch spoken human-computer conversations. Since there exists no Dutch (dialogue) corpus annotated for negation we have annotated a Dutch corpus sample to evaluate our method for automatic negation detection. We use transfer learning and trained NegBERT (an existing BERT implementation used for negation detection) on English data with multilingual BERT to detect negation in Dutch dialogues. Our results show that adding in-domain training material improves the results. We show that we can detect both negation cues and scope in Dutch dialogues with high precision and recall. We provide a detailed error analysis and discuss the effects of cross-lingual and cross-domain transfer learning on automatic negation detection.- Anthology ID:
- 2022.lrec-1.56
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 534–542
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.56
- DOI:
- Cite (ACL):
- Tom Sweers, Iris Hendrickx, and Helmer Strik. 2022. Negation Detection in Dutch Spoken Human-Computer Conversations. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 534–542, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Negation Detection in Dutch Spoken Human-Computer Conversations (Sweers et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.lrec-1.56.pdf