Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages

MCML Authors

Ayyoob Imani

→ Group Hinrich Schütze
 Computational Linguistics

Hinrich Schütze

Prof. Dr.

Principal Investigator

Computational Linguistics

Abstract

Parallel corpora are ideal for extracting a multilingual named entity (MNE) resource, i.e., a dataset of names translated into multiple languages. Prior work on extracting MNE datasets from parallel corpora required resources such as large monolingual corpora or word aligners that are unavailable or perform poorly for underresourced languages. We present CLC-BN, a new method for creating an MNE resource, and apply it to the Parallel Bible Corpus, a corpus of more than 1000 languages. CLC-BN learns a neural transliteration model from parallel-corpus statistics, without requiring any other bilingual resources, word aligners, or seed data. Experimental results show that CLC-BN clearly outperforms prior work. We release an MNE resource for 1340 languages and demonstrate its effectiveness in two downstream tasks: knowledge graph augmentation and bilingual lexicon induction.

inproceedings

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LREC 2022

13th International Conference on Language Resources and Evaluation. Marseille, France, Jun 21-23, 2022.

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Authors

S. Severini • A. Imani • P. Dufter • H. Schütze

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Links

URL

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Research Area

 B2 | Natural Language Processing