We present the first Universal Dependencies treebank for Uzbek, a low-resource language from the Turkic family. The treebank contains 500 sentences (5850 tokens) sourced from the news and fiction genres and it is annotated for lemmas, part-of-speech (POS) tags, morphological features, and dependency relations. We describe our methodology for building the treebank, which consists of a mix of manual and automatic annotation and discuss some constructions of the Uzbek language that pose challenges to the UD framework.

We present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.–19th c.) annotated with Universal Dependencies. We describe several steps of data pre-processing and evaluate the resulting parsing accuracy showing how our pre-processing steps significantly improve output quality. As a sanity check for the representativity of our data, we conduct a case study comparing previously gained insights on grammatical change in the scientific genre with our data. Our results reflect the often reported trend of English scientific discourse towards heavy noun phrases and a simplification of the sentence structure (Halliday, 1988; Halliday and Martin, 1993; Biber and Gray, 2011; Biber and Gray, 2016). We also show that this trend applies to German scientific discourse as well. The presented corpora are valuable resources suitable for the contrastive analysis of syntactic diachronic change in the scientific genre between 1650 and 1900. The presented pre-processing procedures and their evaluations are applicable to other languages and can be useful for a variety of Natural Language Processing tasks such as syntactic parsing.

We describe a methodology to extract with finer accuracy word order patterns from texts automatically annotated with Universal Dependency (UD) trained parsers. We use the methodology to quantify the word order entropy of determiners, quantifiers and numerals in ten Indo-European languages, using UD-parsed texts from a parallel corpus of prosaic texts. Our results suggest that the combinations of different UD annotation layers, such as UD Relations, Universal Parts of Speech and lemma, and the introduction of language-specific lists of closed-category lemmata has the two-fold effect of improving the quality of analysis and unveiling hidden areas of variability in word order patterns.