# E-NER -- An Annotated Named Entity Recognition Corpus of Legal Text

arXiv: https://arxiv.org/abs/2212.09306 · HTML: https://mlsanity.com/paper/2212.09306/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 52 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [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)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [FiNER: Financial Numeric Entity Recognition for XBRL Tagging](https://mlsanity.com/paper/2203.06482/) (2022)
- [FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigm](https://mlsanity.com/paper/2205.15485/) (2022)

## Built on by (most cited first, 13 of 13)

- [Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations](https://mlsanity.com/paper/2306.04618/) (2023)
- [UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition](https://mlsanity.com/paper/2308.03279/) (2023)
- [Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges](https://mlsanity.com/paper/2410.21306/) (2024)
- [LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Text](https://mlsanity.com/paper/2410.12064/) (2024)
- [Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models](https://mlsanity.com/paper/2506.22813/) (2025)
- [Learning Dynamics of Meta-Learning in Small Model Pretraining](https://mlsanity.com/paper/2508.02189/) (2025)
- [Meta-Pretraining for Zero-Shot Cross-Lingual Named Entity Recognition in Low-Resource Philippine Languages](https://mlsanity.com/paper/2509.02160/) (2025)
- [An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains](https://mlsanity.com/paper/2411.14551/) (2024)
- [Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Dataset](https://mlsanity.com/paper/2603.02150/) (2026)
- [Legal NLP Meets MiCAR: Advancing the Analysis of Crypto White Papers](https://mlsanity.com/paper/2310.10333/) (2023)
- [The GELATO Dataset for Legislative NER](https://mlsanity.com/paper/2603.14130/) (2026)
- [CrimeNER Demo: Named-Entity Recognition in the Crime Domain](https://mlsanity.com/paper/2607.14800/) (2026)
- [ANNOTARES: A Dataset for Extracting Logical Structures from German Statutory Texts](https://mlsanity.com/paper/2608.03898/) (2026)

## Abstract (arXiv)

Identifying named entities such as a person, location or organization, in documents can highlight key information to readers. Training Named Entity Recognition (NER) models requires an annotated data set, which can be a time-consuming labour-intensive task. Nevertheless, there are publicly available NER data sets for general English. Recently there has been interest in developing NER for legal text. However, prior work and experimental results reported here indicate that there is a significant degradation in performance when NER methods trained on a general English data set are applied to legal text. We describe a publicly available legal NER data set, called E-NER, based on legal company filings available from the US Securities and Exchange Commission's EDGAR data set. Training a number of different NER algorithms on the general English CoNLL-2003 corpus but testing on our test collection confirmed significant degradations in accuracy, as measured by the F1-score, of between 29.4% and 60.4%, compared to training and testing on the E-NER collection.