Abstract
Nominalization re-writes a clause as a noun phrase. It requires the transformation of the head verb of the clause into a deverbal noun, and the verb’s modifiers into nominal modifiers. Past research has focused on the selection of deverbal nouns, but has paid less attention to predicting the word positions and word forms for the nominal modifiers. We propose the use of a textual entailment model for clause nominalization. We obtained the best performance by fine-tuning a textual entailment model on this task, outperforming a number of unsupervised approaches using language model scores from a state-of-the-art neural language model.- Anthology ID:
- 2022.coling-1.524
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6002–6006
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.524
- DOI:
- Cite (ACL):
- John S. Y. Lee, Ho Hung Lim, Carol Webster, and Anton Melser. 2022. Automatic Nominalization of Clauses through Textual Entailment. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6002–6006, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Automatic Nominalization of Clauses through Textual Entailment (Lee et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.coling-1.524.pdf