EDGAR-CORPUS: Billions of Tokens Make The World Go Round

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.

arXiv comment6 pages, short paper at ECONLP 2021 Workshop, in conjunction with EMNLP 2021

References in corpus (2)

• Efficient Estimation of Word Representations in Vector Space
• Distributed Representations of Words and Phrases and their Compositionality

Cited by in corpus (1)

• KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents