Detecting syntactic differences automatically using the Minimum Description Length principle

  • Martin Kroon Universiteit Leiden
  • Sjef Barbiers Universiteit Leiden
  • Jan Odijk Universiteit Utrecht
  • Stéphanie van der Pas Universiteit Leiden

Abstract

In this paper we present a systematic approach to detect and rank hypotheses about possible syntactic differences for further investigation by leveraging parallel data and using the Minimum Description Length (MDL) principle. We deploy the SQS-algorithm (‘Summarising event seQuenceS’; Tatti and Vreeken 2012) – an MDL-based algorithm – to mine ‘typical’ sequences of Part of Speech (POS) tags for each language under investigation. We create a shortlist of potential syntactic differences based on the number of parallel sentences with a mismatch in pattern occurrence. We applied our method to parallel corpora of English, Dutch and Czech sentences from the Europarl v7 corpus (Koehn 2005). The approach proved useful in both retrieving POS building blocks of a language as well as pointing to meaningful syntactic differences between languages. Despite a clear sensitivity to tagging accuracy, our results and approach are promising

Published
2020-12-12
How to Cite
Kroon, M., Barbiers, S., Odijk, J., & van der Pas, S. (2020). Detecting syntactic differences automatically using the Minimum Description Length principle. Computational Linguistics in the Netherlands Journal, 10, 109-127. Retrieved from https://clinjournal.org/clinj/article/view/109
Section
Articles

Most read articles by the same author(s)