Papers by Alina Cristea

2 papers
Verba volant, scripta volant? Don’t worry! There are computational solutions for protoword reconstruction (2024.emnlp-main)

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Challenge: Existing methods for protoword reconstruction are limited to a few languages.
Approach: They propose a new database of cognate words and etymons for the five main Romance languages and apply machine learning to it.
Outcome: The proposed model achieves 90% accuracy in predicting protowords for Romance languages, surpassing state-of-the-art models and features.
RoBoCoP: A Comprehensive ROmance BOrrowing COgnate Package and Benchmark for Multilingual Cognate Identification (2023.emnlp-main)

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Challenge: Existing databases for romance cognates are scattered, incomplete, noisy, or have uncertain availability.
Approach: They propose to use etymological information to identify Romance cognates and borrowings from dictionaries to identify their ethymology.
Outcome: The proposed method achieves 94% accuracy on two pairs of Romance languages.

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