How Much Does Corpus Choice Change Dependency-Distance Estimates?

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A study in the field of natural language processing published on September 7, 2026, explored the impact of corpus selection on the estimated dependency-distance. The study analyzed data from different corpus sources and found that the choice of corpus directly affected the values of the dependency-distance calculated by the model. This finding indicates that when evaluating syntactic structure complexity using statistical or computational methods, it is necessary to strictly consider and standardize the types of corpora used; otherwise, the conclusions drawn may be biased.

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Universal Dependencies

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Universal Dependencies1

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A arXiv cs.CL en 2026-09-07 12:00

A study compared the average dependency distance estimates for 38 language-specific tree pairs in the Universal Dependency Treebank v2.18. The results showed that consistency across treebanks was extremely poor: replacing a treebank would reverse nearly 40% of language rankings, and treebank selection explained approximately 29% of the inter-group variance. This difference exceeded the sampling error within the treebanks and persisted under twelve preprocessing specifications. Although all treebanks confirmed minimal dependency length (normalized ratios below 1), the data supported the view that dependency distances are a composite of grammatical, genre, and annotation factors influenced by corpus conditions, rather than stable language-level parameters; the qualitative dependency length model remained valid after replacing the corpus, but cross-linguistic ordinal rankings no longer held.