# FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigm

arXiv: https://arxiv.org/abs/2205.15485 · HTML: https://mlsanity.com/paper/2205.15485/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 41 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://mlsanity.com/paper/1806.08730/) (2018)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [Entity-Relation Extraction as Multi-Turn Question Answering](https://mlsanity.com/paper/1905.05529/) (2019)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [ReasoNet: Learning to Stop Reading in Machine Comprehension](https://mlsanity.com/paper/1609.05284/) (2016)
- [Structured prediction models for RNN based sequence labeling in clinical text](https://mlsanity.com/paper/1608.00612/) (2016)
- [Representation Learning for Natural Language Processing](https://mlsanity.com/paper/2102.03732/) (2021)

## Built on by (most cited first, 8 of 8)

- [INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent](https://mlsanity.com/paper/2412.18174/) (2024)
- [FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading](https://mlsanity.com/paper/2502.11433/) (2025)
- [E-NER -- An Annotated Named Entity Recognition Corpus of Legal Text](https://mlsanity.com/paper/2212.09306/) (2022)
- [Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach](https://mlsanity.com/paper/2407.15788/) (2024)
- [Industry Risk Assessment via Hierarchical Financial Data Using Stock Market Sentiment Indicators](https://mlsanity.com/paper/2303.02707/) (2023)
- [Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach](https://mlsanity.com/paper/2309.11027/) (2023)
- [Financial Risk Relation Identification through Dual-view Adaptation](https://mlsanity.com/paper/2509.18775/) (2025)
- [Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition](https://mlsanity.com/paper/2401.11431/) (2024)

## Abstract (arXiv)

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.