Datasets:
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1M - 10M
ArXiv:
License:
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README.md
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### Dataset Summary
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Unlike other entity extraction tasks, like named entity recognition (NER) or contract element extraction, which typically require identifying entities of a small set of common types (e.g., persons, organizations), FiNER-139 uses a much larger label set of
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Another important difference from typical entity extraction is that FiNER focuses on numeric tokens, with the correct tag depending mostly on context, not the token itself.
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To promote transparency among shareholders and potential investors, publicly traded companies are required to file periodic financial reports annotated with tags from the eXtensive Business Reporting Language (XBRL), an XML-based language, to facilitate the processing of financial information.
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However, manually tagging reports with XBRL tags is tedious and resource-intensive.
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We, therefore, introduce
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To facilitate research towards automated XBRL tagging we release FiNER-139.
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### Dataset Summary
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<strong>FiNER-139</strong> is comprised of 1.1M sentences annotated with <strong>eXtensive Business Reporting Language (XBRL)</strong> tags extracted from annual and quarterly reports of publicly-traded companies in the US.
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Unlike other entity extraction tasks, like named entity recognition (NER) or contract element extraction, which typically require identifying entities of a small set of common types (e.g., persons, organizations), FiNER-139 uses a much larger label set of <strong>139 entity types</strong>.
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Another important difference from typical entity extraction is that FiNER focuses on numeric tokens, with the correct tag depending mostly on context, not the token itself.
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</div>
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<div style="text-align: justify">
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To promote transparency among shareholders and potential investors, publicly traded companies are required to file periodic financial reports annotated with tags from the eXtensive Business Reporting Language (XBRL), an XML-based language, to facilitate the processing of financial information.
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However, manually tagging reports with XBRL tags is tedious and resource-intensive.
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We, therefore, introduce <strong>XBRL tagging</strong> as a <strong>new entity extraction task</strong> for the <strong>financial domain</strong> and study how financial reports can be automatically enriched with XBRL tags.
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To facilitate research towards automated XBRL tagging we release FiNER-139.
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