Lefteris Loukas
2021
DICoE@FinSim-3: Financial Hypernym Detection using Augmented Terms and Distance-based Features
Lefteris Loukas
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Konstantinos Bougiatiotis
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Manos Fergadiotis
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Dimitris Mavroeidis
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Elias Zavitsanos
Proceedings of the Third Workshop on Financial Technology and Natural Language Processing
EDGAR-CORPUS: Billions of Tokens Make The World Go Round
Lefteris Loukas
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Manos Fergadiotis
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Ion Androutsopoulos
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Prodromos Malakasiotis
Proceedings of the Third Workshop on Economics and Natural Language Processing
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.
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