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        <title>Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages
%A Severini, Silvia
%A ImaniGooghari, Ayyoob
%A Dufter, Philipp
%A Schütze, Hinrich
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F severini-etal-2022-towards
%X 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.
%U https://aclanthology.org/2022.lrec-1.417
%P 3923-3933

Markdown (Informal)

[Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages](https://aclanthology.org/2022.lrec-1.417) (Severini et al., LREC 2022)

• Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages (Severini et al., LREC 2022)

ACL

• Silvia Severini, Ayyoob ImaniGooghari, Philipp Dufter, and Hinrich Schütze. 2022. Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3923–3933, Marseille, France. European Language Resources Association.