  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 https://github.com/zyz0000/FinBERT-MRC.
