Comparing Symbolic Models of Language via Bayesian Inference (Student Abstract)

Authors

  • Annika Heuser Massachusetts Institute of Technology
  • Polina Tsvilodub Osnabrück University

DOI:

https://doi.org/10.1609/aaai.v35i18.17896

Keywords:

Computational Cognitive Modeling, Computational Linguistics, Bayesian Inference, Knowledge Representation, Probabilistic Reasoning, Statistical Learning

Abstract

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.
AAAI-21 Proceedings Cover

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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

Vol. 35 No. 18: AAAI-21 Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program