Aligning Alignments: Do Colexification and Distributional Similarity Align as Measures of cross-lingual Lexical Alignment?

Taelin Karidi, Eitan Grossman, Omri Abend

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The data-driven investigation of the extent to which lexicons of different languages align has mostly fallen into one of two categories: colexification-based and distributional. The two approaches are grounded in distinct methodologies, operate on different assumptions, and are used in diverse ways. This raises two important questions: (a) are there settings in which the predictions of the two approaches can be directly compared? and if so, (b) what is the extent of the similarity and what are its determinants? We offer novel operationalizations for the two approaches in a manner that allows for their direct comparison, and conduct a comprehensive analysis on a diverse set of 16 languages. Our analysis is carried out at different levels of granularity. At the word-level, the two methods present different results across the board. However, intriguingly, at the level of semantic domains (e.g., kinship, quantity), the two methods show considerable convergence in their predictions. Our findings also indicate that the distributional methods likely capture a more fine-grained alignment than their counterpart colexification-based methods, and may thus be more suited for settings where fewer languages are evaluated.

Original languageEnglish
Title of host publicationCoNLL 2024 - 28th Conference on Computational Natural Language Learning, Proceedings of the Conference
EditorsLibby Barak, Malihe Alikhani
PublisherAssociation for Computational Linguistics (ACL)
Pages327-341
Number of pages15
ISBN (Electronic)9798891761780
StatePublished - 2024
Event28th Conference on Computational Natural Language Learning, CoNLL 2024 - Miami, United States
Duration: 15 Nov 202416 Nov 2024

Publication series

NameCoNLL 2024 - 28th Conference on Computational Natural Language Learning, Proceedings of the Conference

Conference

Conference28th Conference on Computational Natural Language Learning, CoNLL 2024
Country/TerritoryUnited States
CityMiami
Period15/11/2416/11/24

Bibliographical note

Publisher Copyright:
© 2024 Association for Computational Linguistics.

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