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--- |
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dataset_size: 1927946343 |
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annotations_creators: |
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- no-annotation |
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language: |
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- en |
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language_creators: |
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- other |
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license: |
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- apache-2.0 |
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multilinguality: |
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- monolingual |
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pretty_name: EDGAR-CORPUS (10-K Filings from 1999 to 2020) |
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size_categories: |
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- 100K<n<1M |
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source_datasets: |
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- extended|other |
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tags: |
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- research papers |
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- edgar |
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- sec |
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- finance |
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- financial |
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- filings |
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- 10K |
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- 10-K |
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- nlp |
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- research |
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- econlp |
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- economics |
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- business |
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task_categories: |
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- other |
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task_ids: [] |
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--- |
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# Dataset Card for [EDGAR-CORPUS] |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks](#supported-tasks) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Licensing Information](#licensing-information) |
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- [References](#references) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **Point of Contact: Lefteris Loukas** |
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### Dataset Summary |
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This dataset card is based on the paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round** authored by _Lefteris Loukas et.al_, as published in the _ECONLP 2021_ workshop. |
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This dataset contains the annual reports of public companies from 1993-2020 from SEC EDGAR filings. |
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There is supported functionality to load a specific year. |
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Care: since this is a corpus dataset, different `train/val/test` splits do not have any special meaning. It's the default HF card format to have train/val/test splits. |
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If you wish to load specific year(s) of specific companies, you probably want to use the open-source software which generated this dataset, EDGAR-CRAWLER: https://github.com/nlpaueb/edgar-crawler. |
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## Citation |
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If this work helps or inspires you in any way, please consider citing the relevant paper published at the [3rd Economics and Natural Language Processing (ECONLP) workshop](https://lt3.ugent.be/econlp/) at EMNLP 2021 (Punta Cana, Dominican Republic): |
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``` |
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@inproceedings{loukas-etal-2021-edgar, |
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title = "{EDGAR}-{CORPUS}: Billions of Tokens Make The World Go Round", |
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author = "Loukas, Lefteris and |
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Fergadiotis, Manos and |
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Androutsopoulos, Ion and |
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Malakasiotis, Prodromos", |
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booktitle = "Proceedings of the Third Workshop on Economics and Natural Language Processing", |
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month = nov, |
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year = "2021", |
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address = "Punta Cana, Dominican Republic", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.econlp-1.2", |
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pages = "13--18", |
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} |
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``` |
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### Supported Tasks |
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This is a raw dataset/corpus for financial NLP. |
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As such, there are no annotations or labels. |
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### Languages |
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The EDGAR Filings are in English. |
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## Dataset Structure |
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### Data Instances |
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Refer to the dataset preview. |
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### Data Fields |
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**filename**: Name of file on EDGAR from which the report was extracted.<br> |
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**cik**: EDGAR identifier for a firm.<br> |
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**year**: Year of report.<br> |
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**section_1**: Corressponding section of the Annual Report.<br> |
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**section_1A**: Corressponding section of the Annual Report.<br> |
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**section_1B**: Corressponding section of the Annual Report.<br> |
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**section_2**: Corressponding section of the Annual Report.<br> |
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**section_3**: Corressponding section of the Annual Report.<br> |
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**section_4**: Corressponding section of the Annual Report.<br> |
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**section_5**: Corressponding section of the Annual Report.<br> |
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**section_6**: Corressponding section of the Annual Report.<br> |
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**section_7**: Corressponding section of the Annual Report.<br> |
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**section_7A**: Corressponding section of the Annual Report.<br> |
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**section_8**: Corressponding section of the Annual Report.<br> |
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**section_9**: Corressponding section of the Annual Report.<br> |
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**section_9A**: Corressponding section of the Annual Report.<br> |
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**section_9B**: Corressponding section of the Annual Report.<br> |
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**section_10**: Corressponding section of the Annual Report.<br> |
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**section_11**: Corressponding section of the Annual Report.<br> |
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**section_12**: Corressponding section of the Annual Report.<br> |
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**section_13**: Corressponding section of the Annual Report.<br> |
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**section_14**: Corressponding section of the Annual Report.<br> |
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**section_15**: Corressponding section of the Annual Report.<br> |
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|
|
```python |
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import datasets |
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# Load the entire dataset |
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raw_dataset = datasets.load_dataset("eloukas/edgar-corpus", "full") |
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# Load a specific year and split |
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year_1993_training_dataset = datasets.load_dataset("eloukas/edgar-corpus", "year_1993", split="train") |
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``` |
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|
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### Data Splits |
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|
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| Config | Training | Validation | Test | |
|
| --------- | -------- | ---------- | ------ | |
|
| full | 176,289 | 22,050 | 22,036 | |
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| year_1993 | 1,060 | 133 | 133 | |
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| year_1994 | 2,083 | 261 | 260 | |
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| year_1995 | 4,110 | 514 | 514 | |
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| year_1996 | 7,589 | 949 | 949 | |
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| year_1997 | 8,084 | 1,011 | 1,011 | |
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| year_1998 | 8,040 | 1,006 | 1,005 | |
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| year_1999 | 7,864 | 984 | 983 | |
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| year_2000 | 7,589 | 949 | 949 | |
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| year_2001 | 7,181 | 898 | 898 | |
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| year_2002 | 6,636 | 830 | 829 | |
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| year_2003 | 6,672 | 834 | 834 | |
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| year_2004 | 7,111 | 889 | 889 | |
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| year_2005 | 7,113 | 890 | 889 | |
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| year_2006 | 7,064 | 883 | 883 | |
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| year_2007 | 6,683 | 836 | 835 | |
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| year_2008 | 7,408 | 927 | 926 | |
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| year_2009 | 7,336 | 917 | 917 | |
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| year_2010 | 7,013 | 877 | 877 | |
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| year_2011 | 6,724 | 841 | 840 | |
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| year_2012 | 6,479 | 810 | 810 | |
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| year_2013 | 6,372 | 797 | 796 | |
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| year_2014 | 6,261 | 783 | 783 | |
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| year_2015 | 6,028 | 754 | 753 | |
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| year_2016 | 5,812 | 727 | 727 | |
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| year_2017 | 5,635 | 705 | 704 | |
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| year_2018 | 5,508 | 689 | 688 | |
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| year_2019 | 5,354 | 670 | 669 | |
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| year_2020 | 5,480 | 686 | 685 | |
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## Dataset Creation |
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### Source Data |
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#### Initial Data Collection and Normalization |
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Initial data was collected and processed by the authors of the research paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round**. |
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#### Who are the source language producers? |
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Public firms filing with the SEC. |
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### Annotations |
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#### Annotation process |
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NA |
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#### Who are the annotators? |
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NA |
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### Personal and Sensitive Information |
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The dataset contains public filings data from SEC. |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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Low to none. |
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### Discussion of Biases |
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The dataset is about financial information of public companies and as such the tone and style of text is in line with financial literature. |
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### Other Known Limitations |
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The dataset needs further cleaning for improved performance. |
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## Additional Information |
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### Licensing Information |
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EDGAR data is publicly available. |
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### Shoutout |
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Huge shoutout to [@JanosAudran](https://huggingface.co/JanosAudran) for the HF Card setup! |
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### References |
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- [Research Paper] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CORPUS: Billions of Tokens Make The World Go Round. Third Workshop on Economics and Natural Language Processing (ECONLP). https://arxiv.org/abs/2109.14394 - Punta Cana, Dominican Republic, November 2021. |
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- [Software] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CRAWLER. https://github.com/nlpaueb/edgar-crawler (2021) |
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- [EDGAR CORPUS, but in zip files] EDGAR CORPUS: A corpus for financial NLP research, built from SEC's EDGAR. https://zenodo.org/record/5528490 (2021) |
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- [Word Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS. https://zenodo.org/record/5524358 (2021) |
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- [Applied Research paper where EDGAR-CORPUS is used] Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and, George Paliouras. FiNER: Financial Numeric Entity Recognition for XBRL Tagging. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/2022.acl-long.303 (2022) |
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