| Challenge: | Existing cognate databases have limited practical applications for research, despite their wide coverage and limited use in lexical tasks. |
| Approach: | They introduce a new large-scale lexical database that provides cognates across languages. |
| Outcome: | The proposed database contains 3.1 million cognate pairs across 338 languages and has an accuracy of 94%. |
Similar Papers
RoBoCoP: A Comprehensive ROmance BOrrowing COgnate Package and Benchmark for Multilingual Cognate Identification (2023.emnlp-main)
Copied to clipboard
Liviu Dinu, Ana Uban, Alina Cristea, Anca Dinu, Ioan-Bogdan Iordache, Simona Georgescu, Laurentiu Zoicas
| 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. |
An Automated Framework for Fast Cognate Detection and Bayesian Phylogenetic Inference in Computational Historical Linguistics (P19-1)
Copied to clipboard
| Challenge: | Existing methods for phylogenetic reconstruction of large datasets require time and computational power. |
| Approach: | They propose a workflow for phylogenetic reconstruction on large datasets using two methods . they use a method for fast detection of cognates and a Bayesian method for inference . their results show that the methods take less than a few minutes to process language families . |
| Outcome: | The proposed methods are fast and easy to use and close to gold standard cognate judgments and expert language family trees. |
Connecting Language Technologies with Rich, Diverse Data Sources Covering Thousands of Languages (2024.lrec-main)
Copied to clipboard
Daan van Esch, Sandy Ritchie, Sebastian Ruder, Julia Kreutzer, Clara Rivera, Ishank Saxena, Isaac Caswell
| Challenge: | Existing data sources for many thousands of languages are rich and diverse . Efforts are ongoing to extend technology to many more of the world's languages . |
| Approach: | They provide an overview of some of the major online data sources available for thousands of languages. |
| Outcome: | The proposed language technologies are based on the data available for thousands of languages. |
Creating Large-Scale Multilingual Cognate Tables (L18-1)
Copied to clipboard
| Challenge: | Low-resource languages often suffer from a lack of high-coverage lexical resources. |
| Approach: | They propose a method to generate cognate tables by clustering words from existing lexical resources. |
| Outcome: | The proposed method outperforms baselines on the Romance and Turkic language families. |
Verba volant, scripta volant? Don’t worry! There are computational solutions for protoword reconstruction (2024.emnlp-main)
Copied to clipboard
Liviu Dinu, Ana Uban, Alina Cristea, Ioan-Bogdan Iordache, Teodor-George Marchitan, Simona Georgescu, Laurentiu Zoicas
| 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. |
Challenge Dataset of Cognates and False Friend Pairs from Indian Languages (2020.lrec-1)
Copied to clipboard
| Challenge: | Cognates are words that have a common etymological origin and can facilitate the Second Language Acquisition (SLA) however, they also pose a challenge to various NLP applications such as Machine Translation and Cross-lingual Sense Disambiguation. |
| Approach: | They create two cognate datasets for twelve Indian languages and use them to generate cognate sets. |
| Outcome: | The proposed datasets are curated using previously available baseline cognate detection approaches and evaluated with the help of lexicographers. |
Writing System and Speaker Metadata for 2,800+ Language Varieties (2022.lrec-1)
Copied to clipboard
| Challenge: | Currently, language technologies are easily available in only a small minority of the world's 7,000+ language varieties. |
| Approach: | They propose to use an open-source dataset to provide the writing system(s) for each of the 2,800+ languages used in the world today and an estimated speaker count for each. |
| Outcome: | The dataset provides the attested writing system(s) for each of these 2,800+ varieties, as well as an estimated speaker count for each variety. |
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages (2020.coling-main)
Copied to clipboard
Diptesh Kanojia, Raj Dabre, Shubham Dewangan, Pushpak Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni
| Challenge: | a study of 14 Indian languages shows that cognates can be detected by word embeddings . cognates are variants of the same lexical form across languages . |
| Approach: | They propose to use cross-lingual word embeddings to detect cognates among 14 Indian languages . they then evaluate the impact of their method on neural machine translation . |
| Outcome: | The proposed method improves on a dataset of 12 Indian languages . it also improves quality of the extracted cognates by up to 2.76 BLEU . |
LinguaMeta: Unified Metadata for Thousands of Languages (2024.lrec-main)
Copied to clipboard
| Challenge: | LinguaMeta is a unified repository of language metadata for thousands of languages. |
| Approach: | They introduce LinguaMeta, a unified resource for language metadata for thousands of languages. |
| Outcome: | The proposed resource is intended for use by researchers and organizations who aim to extend technology to thousands of languages. |
Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics? (N18-2)
Copied to clipboard
| Challenge: | Phylogenetic trees are hypotheses of how sets of related languages evolved in time. |
| Approach: | They compare the performance of automatic cognate detection algorithms to classical manually annotated cognate sets. |
| Outcome: | The proposed methods perform better than classically annotated cognate sets . future work on phylogenetic reconstruction can profit from the results . |