# FiNER: Financial Numeric Entity Recognition for XBRL Tagging

arXiv: https://arxiv.org/abs/2203.06482 · HTML: https://mlsanity.com/paper/2203.06482/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 60 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [Injecting Numerical Reasoning Skills into Language Models](https://mlsanity.com/paper/2004.04487/) (2020)
- [Representing Numbers in NLP: a Survey and a Vision](https://mlsanity.com/paper/2103.13136/) (2021)
- [Do Language Embeddings Capture Scales?](https://mlsanity.com/paper/2010.05345/) (2020)
- [An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels](https://mlsanity.com/paper/2010.01653/) (2020)
- [EDGAR-CORPUS: Billions of Tokens Make The World Go Round](https://mlsanity.com/paper/2109.14394/) (2021)
- [Subword Pooling Makes a Difference](https://mlsanity.com/paper/2102.10864/) (2021)
- [Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks](https://mlsanity.com/paper/1706.03993/) (2017)
- [Financial Event Extraction Using Wikipedia-Based Weak Supervision](https://mlsanity.com/paper/1911.10783/) (2019)
- [Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction](https://mlsanity.com/paper/1910.05032/) (2019)
- [Neural Contract Element Extraction Revisited: Letters from Sesame Street](https://mlsanity.com/paper/2101.04355/) (2021)
- [Layer-wise Guided Training for BERT: Learning Incrementally Refined Document Representations](https://mlsanity.com/paper/2010.05763/) (2020)
- [Large-Scale Classification of Structured Objects using a CRF with Deep Class Embedding](https://mlsanity.com/paper/1705.07420/) (2017)

## Built on by (most cited first, 30 of 50)

- [Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models](https://mlsanity.com/paper/2510.04618/) (2025)
- [Meta-Harness: End-to-End Optimization of Model Harnesses](https://mlsanity.com/paper/2603.28052/) (2026)
- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [A Survey of Large Language Models in Finance (FinLLMs)](https://mlsanity.com/paper/2402.02315/) (2024)
- [Meta Context Engineering via Agentic Skill Evolution](https://mlsanity.com/paper/2601.21557/) (2026)
- [Simulating Financial Market via Large Language Model based Agents](https://mlsanity.com/paper/2406.19966/) (2024)
- [FiNER-ORD: Financial Named Entity Recognition Open Research Dataset](https://mlsanity.com/paper/2302.11157/) (2023)
- [SEC-QA: A Systematic Evaluation Corpus for Financial QA](https://mlsanity.com/paper/2406.14394/) (2024)
- [When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents](https://mlsanity.com/paper/2510.11695/) (2025)
- [FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs](https://mlsanity.com/paper/2510.08886/) (2025)
- [E-NER -- An Annotated Named Entity Recognition Corpus of Legal Text](https://mlsanity.com/paper/2212.09306/) (2022)
- [Financial Numeric Extreme Labelling: A Dataset and Benchmarking for XBRL Tagging](https://mlsanity.com/paper/2306.03723/) (2023)
- [Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance](https://mlsanity.com/paper/2308.14634/) (2023)
- [FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets](https://mlsanity.com/paper/2505.19819/) (2025)
- [Combee: Scaling Prompt Learning for Self-Improving Language Model Agents](https://mlsanity.com/paper/2604.04247/) (2026)
- [Benchmarking Multi-Agent LLM Architectures for Financial Document Processing: A Comparative Study of Orchestration Patterns, Cost-Accuracy Tradeoffs and Production Scaling Strategies](https://mlsanity.com/paper/2603.22651/) (2026)
- [PIIBench: A Unified Multi-Source Benchmark Corpus for Personally Identifiable Information Detection](https://mlsanity.com/paper/2604.15776/) (2026)
- [Self-Specialization: Uncovering Latent Expertise within Large Language Models](https://mlsanity.com/paper/2310.00160/) (2023)
- [Enterprise Benchmarks for Large Language Model Evaluation](https://mlsanity.com/paper/2410.12857/) (2024)
- [Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling](https://mlsanity.com/paper/2405.06671/) (2024)
- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [FinSheet-Bench: From Simple Lookups to Complex Reasoning, Where LLMs Break on Financial Spreadsheets](https://mlsanity.com/paper/2603.07316/) (2026)
- [Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models](https://mlsanity.com/paper/2310.13312/) (2023)
- [FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking](https://mlsanity.com/paper/2504.16188/) (2025)
- [FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information](https://mlsanity.com/paper/2505.20650/) (2025)
- [Structure-Aware Decoding Mechanisms for Complex Entity Extraction with Large-Scale Language Models](https://mlsanity.com/paper/2512.13980/) (2025)
- [FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration](https://mlsanity.com/paper/2605.08520/) (2026)
- [Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction](https://mlsanity.com/paper/2608.11772/) (2026)
- [Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models](https://mlsanity.com/paper/2210.13086/) (2022)
- [Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods](https://mlsanity.com/paper/2211.13819/) (2022)

## Abstract (arXiv)

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.