Predicates as Boxes in Bayesian Semantics for Natural Language
Jean-Philippe Bernardy, Rasmus Blanck, Stergios Chatzikyriakidis, Shalom Lappin, Aleksandre Maskharashvili
Abstract
In this paper, we present a Bayesian approach to natural language semantics. Our main focus is on the inference task in an environment where judgments require probabilistic reasoning. We treat nouns, verbs, adjectives, etc. as unary predicates, and we model them as boxes in a bounded domain. We apply Bayesian learning to satisfy constraints expressed as premises. In this way we construct a model, by specifying boxes for the predicates. The probability of the hypothesis (the conclusion) is evaluated against the model that incorporates the premises as constraints.- Anthology ID:
- W19-6137
- Volume:
- Proceedings of the 22nd Nordic Conference on Computational Linguistics
- Month:
- September–October
- Year:
- 2019
- Address:
- Turku, Finland
- Editors:
- Mareike Hartmann, Barbara Plank
- Venue:
- NoDaLiDa
- SIG:
- Publisher:
- Linköping University Electronic Press
- Note:
- Pages:
- 333–337
- Language:
- URL:
- https://aclanthology.org/W19-6137
- DOI:
- Cite (ACL):
- Jean-Philippe Bernardy, Rasmus Blanck, Stergios Chatzikyriakidis, Shalom Lappin, and Aleksandre Maskharashvili. 2019. Predicates as Boxes in Bayesian Semantics for Natural Language. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 333–337, Turku, Finland. Linköping University Electronic Press.
- Cite (Informal):
- Predicates as Boxes in Bayesian Semantics for Natural Language (Bernardy et al., NoDaLiDa 2019)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/W19-6137.pdf