Tweaking UD Annotations to Investigate the Placement of Determiners, Quantifiers and Numerals in the Noun Phrase
Vylomova, Ekaterina; Ponti, Edoardo; Cotterell, Ryan (Ed.): Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, Association for Computational Linguistics, pp. 36-41, Seattle, Washington, 2022.
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.