UCxn: Typologically Informed Annotation of Constructions Atop Universal Dependencies

Repository for the ongoing UCxn Project to add construction information to Universal Dependencies (UD), and code and dataset for "UCxn: Typologically Informed Annotation of Constructions Atop Universal Dependencies" (Weissweiler et al., LREC-COLING 2024). It presents an approach to annotating constructions as a layer on top of UD ("UCxn"), with case studies exploring 5 typologically defined construction families in 10 languages, and Grew rules to automatically infer the construction annotations.

This paper can be found in the docs folder, alongside a full technical specification for the annotation scheme and construction subtypes in Version 1.

Grew queries described by the paper are found in the grew_rules folder, and the automatically annotated corpora in annotated_corpora. In addition, annotations can be explored in Grew-match, e.g. the UCxn-enhanced English-GUM corpus.

The UD 2.13 release served as the input for producing the rule-based annotations in annotated_corpora. (English-GUM contained some pilot construction annotations in the 2.13 release; those were removed before applying the Grew rules.) Individual treebanks are encouraged to incorporate UCxn annotations, ideally with manual checking, into official UD treebank repositories for future releases.

Example Annotation

ID	FORM	LEMMA	UPOS	...	UCxn
1	what	what	PRON	...	CxnElt=2:Interrogative-WHInfo-Direct.WHWord
2	happened	happen	VERB	...	Cxn=Interrogative-WHInfo-Direct|CxnElt=2:Interrogative-WHInfo-Direct.Clause
3	to	to	ADP	...	_
4	you	you	PRON	...	_
5	?	?	PUNCT	...	_

Annotated Data Statistics

Languages

• English
• German
• Swedish
• French
• Spanish
• Portuguese
• Hindi
• Mandarin
• Hebrew
• Coptic

Constructions

• Interrogative
• Existential
• Conditional
• Resultative
• NPN

Citation

If you use the dataset or code, please cite our paper:

@misc{weissweiler2024ucxn,
      title={{UCxn}: Typologically Informed Annotation of Constructions Atop {U}niversal {D}ependencies}, 
      author={Leonie Weissweiler and Nina B\"{o}bel and Kirian Guiller and Santiago Herrera and Wesley Scivetti and Arthur Lorenzi and Nurit Melnik and Archna Bhatia and Hinrich Sch\"{u}tze and Lori Levin and Amir Zeldes and Joakim Nivre and William Croft and Nathan Schneider},
      year={2024},
      eprint={2403.17748},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}