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        <title>EDGAR-CORPUS: Billions of Tokens Make The World Go Round</title>
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        <namePart type="given">Manos</namePart>
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            <namePart type="given">Veronique</namePart>
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        <name type="personal">
            <namePart type="given">Amanda</namePart>
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    <abstract>We release EDGAR-CORPUS, a novel corpus comprising annual reports from all the publicly traded companies in the US spanning a period of more than 25 years. To the best of our knowledge, EDGAR-CORPUS is the largest financial NLP corpus available to date. All the reports are downloaded, split into their corresponding items (sections), and provided in a clean, easy-to-use JSON format. We use EDGAR-CORPUS to train and release EDGAR-W2V, which are WORD2VEC embeddings for the financial domain. We employ these embeddings in a battery of financial NLP tasks and showcase their superiority over generic GloVe embeddings and other existing financial word embeddings. We also open-source EDGAR-CRAWLER, a toolkit that facilitates downloading and extracting future annual reports.</abstract>
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    <identifier type="doi">10.18653/v1/2021.econlp-1.2</identifier>
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%0 Conference Proceedings
%T EDGAR-CORPUS: Billions of Tokens Make The World Go Round
%A Loukas, Lefteris
%A Fergadiotis, Manos
%A Androutsopoulos, Ion
%A Malakasiotis, Prodromos
%Y Hahn, Udo
%Y Hoste, Veronique
%Y Stent, Amanda
%S Proceedings of the Third Workshop on Economics and Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F loukas-etal-2021-edgar
%X We release EDGAR-CORPUS, a novel corpus comprising annual reports from all the publicly traded companies in the US spanning a period of more than 25 years. To the best of our knowledge, EDGAR-CORPUS is the largest financial NLP corpus available to date. All the reports are downloaded, split into their corresponding items (sections), and provided in a clean, easy-to-use JSON format. We use EDGAR-CORPUS to train and release EDGAR-W2V, which are WORD2VEC embeddings for the financial domain. We employ these embeddings in a battery of financial NLP tasks and showcase their superiority over generic GloVe embeddings and other existing financial word embeddings. We also open-source EDGAR-CRAWLER, a toolkit that facilitates downloading and extracting future annual reports.
%R 10.18653/v1/2021.econlp-1.2
%U https://aclanthology.org/2021.econlp-1.2
%U https://doi.org/10.18653/v1/2021.econlp-1.2
%P 13-18

Markdown (Informal)

[EDGAR-CORPUS: Billions of Tokens Make The World Go Round](https://aclanthology.org/2021.econlp-1.2) (Loukas et al., ECONLP 2021)

• EDGAR-CORPUS: Billions of Tokens Make The World Go Round (Loukas et al., ECONLP 2021)

ACL

• Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and Prodromos Malakasiotis. 2021. EDGAR-CORPUS: Billions of Tokens Make The World Go Round. In Proceedings of the Third Workshop on Economics and Natural Language Processing, pages 13–18, Punta Cana, Dominican Republic. Association for Computational Linguistics.