Slovenian Word Associations with Explanations WAX-sl

Name: Slovenian Word Associations with Explanations WAX-sl
License: https://creativecommons.org/licenses/by-sa/4.0/

Vrabl, Klara; Vintar, Špela; Brglez, Mojca

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dc.contributor.author	Vrabl, Klara
dc.contributor.author	Vintar, Špela
dc.contributor.author	Brglez, Mojca
dc.date.accessioned	2026-09-08T08:18:29Z
dc.date.available	2026-09-08T08:18:29Z
dc.date.issued	2026-08-25
dc.identifier.uri	http://hdl.handle.net/11356/2358
dc.description	WAX-sl is the first Slovenian word association dataset that pairs cue–association pairs with a short explanatory sentence and, for a subset of the data, a semantic relation label. It adapts the methodology of the English WAX (Word Association eXplanations) dataset (Liu et al., 2022) to Slovenian. The 100 cue words were randomly sampled from the Reference List of Slovenian Frequent Common Words (Arhar Holdt et al., 2020), balanced by part of speech (34 nouns, 33 verbs, 33 adjectives). Data were elicited in two phases from 9 paid annotators (students of linguistics/digital linguistics), each cue answered by 4 participants: in Phase 1 participants gave up to three free associations per cue, and in Phase 2 they wrote a short sentence (3–15 words) explaining the relation between cue and association. This produced 1,261 cue–association–explanation triples. A subset of 700 triples (covering all 100 cues) was further annotated with a semantic relation label using an extended version of the WAX relation taxonomy, comprising 21 fine-grained labels across five coarse categories (Concept-Properties, Situational, Taxonomic, Linguistic, None-of-the-Above). Each item was independently labelled by two of five trained annotators and adjudicated by a third curator; inter-annotator agreement (pairwise Cohen's κ) was 0.46. The remaining 561 triples were not selected for relation annotation and are marked NA in the Relation column. The dataset is released as a CSV file with the columns: ID (unique triple identifier), CUE (cue word), ASSOCIATION (association given in response to the cue), EXPLANATION (explanatory sentence linking cue and association), and RELATION (semantic relation label, or NA if not annotated). WAX-sl was created as part of the master's thesis "A multilingual comparison of human-generated and LLM-generated associations" (Vrabl, 2026), which also compares this human dataset against associations generated by three LLMs (Gemma 3, GaMS, Magistral Small). References: Holdt, Špela Arhar, Senja Pollak, Marko Robnik Šikonja, and Simon Krek. 2020. ‘Referenčni Seznam Pogostih Splošnih Besed Za Slovenščino’. Conference on Language Technologies & Digital Humanities. http://hdl.handle.net/11356/1346. Liu, Chunhua, Trevor Cohn, Simon De Deyne, and Lea Frermann. 2022. WAX: A New Dataset for Word Association eXplanations. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 106–120, Online only. Association for Computational Linguistics. https://aclanthology.org/2022.aacl-main.9/ Vrabl, Klara. 2026. A multilingual comparison of human-generated and LLM-generated associations. Master's thesis, Faculty of Arts, University of Ljubljana, http://hdl.handle.net/20.500.12556/RUL-184558.
dc.language.iso	slv
dc.publisher	Jožef Stefan Institute
dc.relation.isreferencedby	http://hdl.handle.net/20.500.12556/RUL-184558
dc.rights	Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
dc.rights.uri	https://creativecommons.org/licenses/by-sa/4.0/
dc.rights.label	PUB
dc.subject	word associations
dc.subject	explanations
dc.title	Slovenian Word Associations with Explanations WAX-sl
dc.type	lexicalConceptualResource
metashare.ResourceInfo#ContentInfo.detailedType	other
metashare.ResourceInfo#ContentInfo.mediaType	text
has.files	yes
branding	CLARIN.SI data & tools
contact.person	Špela Vintar spela.vintar@ijs.si Jožef Stefan Institute
sponsor	ARIS (Slovenian Research and Innovation Agency) GC-0002 LLM4DH: Large Language Models for Digital Humanities nationalFunds
size.info	1262 items
files.count	1
files.size	88350