# Named Entity Recognition and Classification on Historical Documents: A Survey

arXiv: https://arxiv.org/abs/2109.11406 · HTML: https://mlsanity.com/paper/2109.11406/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 253 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 30 of 36)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [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)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [Neural Unsupervised Domain Adaptation in NLP---A Survey](https://mlsanity.com/paper/2006.00632/) (2020)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Ultra-Fine Entity Typing](https://mlsanity.com/paper/1807.04905/) (2018)
- [An Analysis of Simple Data Augmentation for Named Entity Recognition](https://mlsanity.com/paper/2010.11683/) (2020)
- [Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT](https://mlsanity.com/paper/2003.04985/) (2020)
- [Few-shot classification in Named Entity Recognition Task](https://mlsanity.com/paper/1812.06158/) (2018)
- [Latin BERT: A Contextual Language Model for Classical Philology](https://mlsanity.com/paper/2009.10053/) (2020)
- [What to do about non-standard (or non-canonical) language in NLP](https://mlsanity.com/paper/1608.07836/) (2016)
- [Design Challenges and Misconceptions in Neural Sequence Labeling](https://mlsanity.com/paper/1806.04470/) (2018)
- [Generalisation in Named Entity Recognition: A Quantitative Analysis](https://mlsanity.com/paper/1701.02877/) (2017)
- [NNE: A Dataset for Nested Named Entity Recognition in English Newswire](https://mlsanity.com/paper/1906.01359/) (2019)
- [Sentiment Analysis in the News](https://mlsanity.com/paper/1309.6202/) (2013)

## Built on by (most cited first, 30 of 47)

- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [AgenticData: An Agentic Data Analytics System for Heterogeneous Data](https://mlsanity.com/paper/2508.05002/) (2025)
- [Deep Learning based Visually Rich Document Content Understanding: A Survey](https://mlsanity.com/paper/2408.01287/) (2024)
- [Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents](https://mlsanity.com/paper/2504.00414/) (2025)
- [Yes but.. Can ChatGPT Identify Entities in Historical Documents?](https://mlsanity.com/paper/2303.17322/) (2023)
- [ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages](https://mlsanity.com/paper/2403.17859/) (2024)
- [Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0](https://mlsanity.com/paper/2204.05211/) (2022)
- [Multilingual Event Extraction from Historical Newspaper Adverts](https://mlsanity.com/paper/2305.10928/) (2023)
- [HistNERo: Historical Named Entity Recognition for the Romanian Language](https://mlsanity.com/paper/2405.00155/) (2024)
- [A Massive Scale Semantic Similarity Dataset of Historical English](https://mlsanity.com/paper/2306.17810/) (2023)
- [ConfusionPrompt: Practical Private Inference for Online Large Language Models](https://mlsanity.com/paper/2401.00870/) (2023)
- [Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition](https://mlsanity.com/paper/2406.05460/) (2024)
- [Reading Order Independent Metrics for Information Extraction in Handwritten Documents](https://mlsanity.com/paper/2404.18664/) (2024)
- [HERITAGE: An End-to-End Web Platform for Processing Korean Historical Documents in Hanja](https://mlsanity.com/paper/2501.11951/) (2025)
- [Musical Heritage Historical Entity Linking](https://mlsanity.com/paper/2502.09168/) (2025)
- [Evaluating Robustness of LLMs in Question Answering on Multilingual Noisy OCR Data](https://mlsanity.com/paper/2502.16781/) (2025)
- [Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone](https://mlsanity.com/paper/2505.20113/) (2025)
- [Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detection](https://mlsanity.com/paper/2511.21066/) (2025)
- [Is text normalization relevant for classifying medieval charters?](https://mlsanity.com/paper/2408.16446/) (2024)
- [A Review on Large Language Models for Visual Analytics](https://mlsanity.com/paper/2503.15176/) (2025)
- [A Comparative Analysis of Word Segmentation, Part-of-Speech Tagging, and Named Entity Recognition for Historical Chinese Sources, 1900-1950](https://mlsanity.com/paper/2503.19844/) (2025)
- [Evaluating Named Entity Recognition Models for Russian Cultural News Texts: From BERT to LLM](https://mlsanity.com/paper/2506.02589/) (2025)
- [MariNER: A Dataset for Historical Brazilian Portuguese Named Entity Recognition](https://mlsanity.com/paper/2506.23051/) (2025)
- [It's All About the Confidence: An Unsupervised Approach for Multilingual Historical Entity Linking using Large Language Models](https://mlsanity.com/paper/2601.08500/) (2026)
- [SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models](https://mlsanity.com/paper/2606.05004/) (2026)
- [People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts](https://mlsanity.com/paper/2305.16718/) (2023)
- [Transfer Learning across Several Centuries: Machine and Historian Integrated Method to Decipher Royal Secretary's Diary](https://mlsanity.com/paper/2306.14592/) (2023)
- [A Novel Method for News Article Event-Based Embedding](https://mlsanity.com/paper/2405.13071/) (2024)

## Abstract (arXiv)

After decades of massive digitisation, an unprecedented amount of historical documents is available in digital format, along with their machine-readable texts. While this represents a major step forward with respect to preservation and accessibility, it also opens up new opportunities in terms of content mining and the next fundamental challenge is to develop appropriate technologies to efficiently search, retrieve and explore information from this 'big data of the past'. Among semantic indexing opportunities, the recognition and classification of named entities are in great demand among humanities scholars. Yet, named entity recognition (NER) systems are heavily challenged with diverse, historical and noisy inputs. In this survey, we present the array of challenges posed by historical documents to NER, inventory existing resources, describe the main approaches deployed so far, and identify key priorities for future developments.