Abstract
In this work, we introduce a generative model, PLC+, for generating Lambek Categorial Grammar(LCG) sequents. We also introduce a simple method to numerically estimate the model’s parameters from an annotated corpus. Then we compare our model with probabilistic context-free grammars (PCFGs) and show that PLC+ simultaneously assigns a higher probability to a common corpus, and has greater coverage.- Anthology ID:
- 2024.lrec-main.50
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 584–593
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.50
- DOI:
- Cite (ACL):
- Jinman Zhao and Gerald Penn. 2024. A Generative Model for Lambek Categorial Sequents. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 584–593, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- A Generative Model for Lambek Categorial Sequents (Zhao & Penn, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.50.pdf