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

Authors

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

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.

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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. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17896

Issue

Section

AAAI Student Abstract and Poster Program