Comparing Symbolic Models of Language via Bayesian Inference (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v35i18.17896Keywords:
Computational Cognitive Modeling, Computational Linguistics, Bayesian Inference, Knowledge Representation, Probabilistic Reasoning, Statistical LearningAbstract
Given recurring interest in structured representations in computational cognitive models, we extend a Bayesian scoring procedure for comparing symbolic models of language grammar. We conduct a case-study of modeling syntactic principles in German, providing preliminary results consistent with linguistic theory. We also note that dataset and part-of-speech (POS) tagger quality should not be taken for granted.Downloads
Published
2021-05-18
How to Cite
Heuser, A., & Tsvilodub, P. (2021). Comparing Symbolic Models of Language via Bayesian Inference (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15799-15800. https://doi.org/10.1609/aaai.v35i18.17896
Issue
Section
AAAI Student Abstract and Poster Program