Publicly traded companies are required to submit periodic reports with
eXtensive Business Reporting Language (XBRL) word-level tags. Manually tagging
the reports is tedious and costly. We, therefore, introduce XBRL tagging as a
new entity extraction task for the financial domain and release FiNER-139, a
dataset of 1.1M sentences with gold XBRL tags. Unlike typical entity extraction
datasets, FiNER-139 uses a much larger label set of 139 entity types. Most
annotated tokens are numeric, with the correct tag per token depending mostly
on context, rather than the token itself. We show that subword fragmentation of
numeric expressions harms BERT's performance, allowing word-level BILSTMs to
perform better. To improve BERT's performance, we propose two simple and
effective solutions that replace numeric expressions with pseudo-tokens
reflecting original token shapes and numeric magnitudes. We also experiment
with FIN-BERT, an existing BERT model for the financial domain, and release our
own BERT (SEC-BERT), pre-trained on financial filings, which performs best.
Through data and error analysis, we finally identify possible limitations to
inspire future work on XBRL tagging.

进行标记是费时且昂贵的，因此该研究引入了 XBRL 标记作为金融领域新的实体提取任务，并发布了 FiNER-139 数据集，其中包含 110 万句子和黄金 XBRL 标记。该研究表明，采用字母级别的词元分割会损害 BERT 的性能，而采用单词级别的 BILSTMs 的性能更佳。此外，该研究还提出了两种简单而有效的解决方案，以改善 BERT 的性能。最终，该研究通过数据和错误分析确定了潜在的限制，为未来的 XBRL 标记工作提供了启示。