Financial named entity recognition (FinNER) from literature is a challenging
task in the field of financial text information extraction, which aims to
extract a large amount of financial knowledge from unstructured texts. It is
widely accepted to use sequence tagging frameworks to implement FinNER tasks.
However, such sequence tagging models cannot fully take advantage of the
semantic information in the texts. Instead, we formulate the FinNER task as a
machine reading comprehension (MRC) problem and propose a new model termed
FinBERT-MRC. This formulation introduces significant prior information by
utilizing well-designed queries, and extracts start index and end index of
target entities without decoding modules such as conditional random fields
(CRF). We conduct experiments on a publicly available Chinese financial dataset
ChFinAnn and a real-word bussiness dataset AdminPunish. FinBERT-MRC model
achieves average F1 scores of 92.78% and 96.80% on the two datasets,
respectively, with average F1 gains +3.94% and +0.89% over some sequence
tagging models including BiLSTM-CRF, BERT-Tagger, and BERT-CRF. The source code
is available at this https URL.

通过对金融文本信息提取的困难进行分析，利用机器阅读理解方法，设计实现了一种新的模型 ——FinBERT-MRC，能更好的提取目标实体和信息。应用于华语和商业数据集，实验结果表明 FinBERT-MRC 模型相比 BiLSTM-CRF、BERT-Tagger、BERT-CRF 等序列标注模型中的 F1 值平均增加了 3.94% 至 0.89%。