New publication within the journal PLOS ONE

@article{Chen:et:al:2026,
  doi       = {10.1371/journal.pone.0346096},
  author    = {Chen, Yanran and Zhao, Wei and Breitbarth, Anne and Stoeckel, Manuel
               and Mehler, Alexander and Schlechtweg, Dominik and Eger, Steffen},
  journal   = {PLOS ONE},
  publisher = {Public Library of Science},
  title     = {Syntactic language change in English and German: Metrics, parsers,
               and convergences},
  year      = {2026},
  month     = {04},
  volume    = {21},
  url       = {https://doi.org/10.1371/journal.pone.0346096},
  pages     = {1-33},
  abstract  = {Syntactic language change has gained increasing attention in recent
               years. Previous computational work based on dependency relations
               has focused on diachronic trends in dependency distance, which
               measures the linear distance between dependent words, using dependency
               trees automatically predicted by a dependency parser (mostly the
               Stanford CoreNLP parser). In this work, we introduce a set of
               15 syntax metrics that extend the analysis beyond linear distance
               by incorporating both linear and tree graph properties of dependency
               trees, such as tree height and degree. Besides, we propose a multi-parser
               approach to reduce the impact of using specific parsers, thereby
               increasing the robustness of the detected language changes. Through
               a cross-lingual investigation of English and German in parliamentary
               debates from the last 160 years, using 6 different parsers (CoreNLP
               and five newer alternatives), we demonstrate that: (1) Relying
               on one single parser can be problematic, as the agreement on predicted
               trends can be low across parsers. (2) Our set of metrics can capture
               subtle patterns of syntactic changes. Our analysis shows that
               syntactic change over the time period inspected is largely similar
               between English and German, with only 2.2% of cases yielding opposite
               trends in these metrics. (3) We also show that changes in syntactic
               metrics seem to be more frequent at the tails of sentence length
               distributions and often move in opposite directions for short
               and long sentences. To our best knowledge, ours is the most comprehensive
               computational analysis of syntactic language change using modern
               NLP technology in recent corpora of English and German.},
  number    = {4}
}