A New State of the Art for Named Entity Recognition

Here at Primer we are building machines that can read, write, and understand natural language text. We measure our progress by breaking that down into smaller cognitive tasks. Usually our progress is incremental. But sometimes we make a giant leap forward.

On the reading task of Named Entity Recognition (NER) we have now surpassed the best-performing models in the industry by a wide margin: with our model achieving a 95.6% F1 accuracy score on CoNLL. This puts us more than two points ahead of a recently published NER model from Facebook AI Research. More importantly, we are now on par with human-level performance. It requires consensus across a team of trained human labelers to reach higher accuracy.


NER performance


Primer’s NER model has surpassed the previous state of the art models of Google and Facebook on F1 accuracy score. Graph adapted from Sebastian Ruder, DeepMind.

NER: What’s in a Name?

Named Entity Recognition (NER) is a foundational task in Natural Language Processing because so many downstream tasks depend on it. The goal of NER is to find all of the named people, places, and things within a text document and correctly classify them.

The gold standard benchmark for NER was laid out in a 2003 academic challenge called CoNLL. The CoNLL data set consists of news articles with all of the named entities hand-labeled by humans. (There is also a German-language CoNLL data set.) This established the four standard NER classes: person (PER), organization (ORG), location (LOC), and miscellaneous (MISC).

The NER labeling task is not as easy as it sounds. Consider this sentence:




After a thoughtful pause, a human reader can deduce that “Paris Hilton” is a person, “the Hilton” is an organization, and “Paris” is a location. (Humans will disagree about 15% of the time whether “the Hilton” should instead be classified as a location.)

A popular industry solution for extracting named entities from text is spaCy. Here is the output of spaCy 2.1 NER:


I_heard_that_Paris 1


Not bad. The spaCy model does correctly identify all of the named entity spans. And it correctly identifies the second “Hilton” and second “Paris” as an organization and location, respectively. But Paris Hilton herself is misclassified as an ORG. So spaCy is only getting 66% accuracy on this text. And on our diverse gold-labeled NER data spaCy 2.1 falls well below 50% accuracy.

In order for models to be useful in a commercial setting, they need far better performance. So some new ideas are needed here.

New models are good, but data diversity is king

To create our own NER model we started with a BERT-based architecture and fine-tuned it for NER with the CoNLL training data. By switching to a universal language model like BERT, we immediately left spaCy in the dust, jumping an average 28 points of precision across all entity classes.

However, that higher precision came at a cost in recall. For example, Primer’s BERT-NER model was not confident enough to tag “Paris Hilton” in this sentence:




Pushing our NER model beyond state of the art required two more innovations. First, we switched to a more powerful universal language model: XLNet. But we discovered that even larger performance gains are possible through data engineering.

The CoNLL NER data set is limited to just one type of text document: Reuters news articles published in 1996 and 1997. This is very low data diversity compared to the internet-scale corpus of documents we process at Primer. We needed our NER model to be trained on a far broader range of writing styles, subject matter, and entities. So we curated a highly diverse group of gold-labeled documents, including entities from the financial, defense-related, and scientific worlds.

Injecting this diversity into our training data made all the difference. Even adversarial examples rarely stump Primer’s NER model:




Since the first universal language models like BERT came out one year ago, we’ve seen a revolution in the field of natural language processing. You can see this rapid progression in the graph above. But take note where Primer’s NER model lands. Our performance on CoNLL stands above the best results published by the enormous research teams at Google, Facebook, and the entire academic community. We have made more progress on NER over the past two months than the entire machine learning field has achieved in the past two years.


Named Entity Recognition (NER)


Primer’s NER model is approaching human-level performance. We find that individual humans disagree on consensus NER labels 15% of the time on average, even after training.


Of the four Entity groups, PERSON extraction has the highest performance with 0.94 precision and 0.95 recall. Location extraction is the second highest with organization third and Miscellaneous the fourth highest ranking. These results mirror the performance of our human evaluators against gold standard data, with humans having the lowest inter-annotator agreement on the miscellaneous and organization categories.

Putting NER to work

So what can you do with the world’s best NER model?

Primer powers analyst workflows in some of the largest organizations in the world. Better NER translates to better downstream natural language processing. It powers coreference resolution to correctly attribute every quote by every politician and CEO in the world. You need it for relation extraction to convert unstructured text that describes entities into structured data—facts about people, places, and organizations. And for text classification, for example identifying corporate risks hidden deep inside a company’s financial documents.

To see how NER works on a text document, consider this transcript of Mark Zuckerberg’s congressional testimony. It takes about 5 seconds to process the 50,000+ words with Primer’s NER model and extract 271 people, places, organizations, and even named entities such as Facebook’s Libra cryptocurrency project. (See the output below.)

For a deeper stress test we’ve been running it on document types that it has never seen before. For an extreme test we turned to Harry Potter fan fiction novels. Because if our model deduces that the Noble House of Potter is an organization, Phobos Malfoy is a person, Libere Loqui is a miscellaneous entity, and Snog Row is a location, then extracting the named entities from business documents should be a walk in the park.

So how does it do? We’re glad you asked. We ran the experiment and here are the results. Below is the output from Zuckerberg’s congressional grilling.

NER output from Mark Zuckerberg’s congressional testimony, 23 October 2019


Cheryl Tipton Perlmutter
Vargus Congressman Casten
Martin Clay Mister
Mister Meeks Cleaver Barr
Hawley Phillips Inaudible
Warner Davidson Chan Zuckerberg
Vargas Gonzalez Mark Zuckerberg
Beatty Riggleman Bud
Chair Waters Pressley Axne
David Zuckerman
Luetkemeyer Huizenga
Mr Zuckerberg Kostoff
Emmer Speaker
Scott Foster
Hollingsworth Ms. Waters
Gonzales Presley
Himes Gottheimer
Green Stivers
Louis Brandeis Loudermilk


FinCEN EU Hamas
Liberty European NCMEC
HUD Facebook ACLU
PayPal congress BFSOC
SEC Newsweek Visa
Alipay Congress Dais
FTC Calibra Rand
FBI Twitter Uber
AML BSA Crypto
Google Lyft ICE
Subcommittee on Oversight and Investigations eBay Nazi
WhatsApp MDIs CFTC
Collibra First FHFA
UN Super LLCs
FINCEN Black Lives Matter Pew Research Center
Dias The New York Times Bookings Holdings
House Fintech Task Force The Washington Post
Ebay Federal Reserve Financial Stability Board
NAACP US Treasury The Department of Justice
US Department of Housing and Urban Development Facebook Libra Cambridge Analytica
G7 Black Mirror Financial Services Committee
MasterCard Congresswoman Hezbollah
Anchorage Trust AI Task Force Senate Intelligence Committee
Trump Hotel Capitol Hill Independent Libra Association
Muslim Advocates Mercado Pago National Fair Housing Alliance
The Capitol Social Network Office of Secretary of Defense
The Daily Caller Trump Hotels Microsoft
regulators The Guardian National Center on Missing and
LIBOR Association New York Times Securities Exchange Commission
Libra Association Wells Fargo Committee on Financial Services
Libra association Congressmen Independent Fact-Checking Network
Labor Association US Congress International Fact-Checking Network
Georgetown European Union Instagram
Congressional United Nations Supreme Court
Trump International Hotel Department of justice Messenger
Federal Reserve Board Federal Housing Agency The Times
WeChat Pay Rainbow Push coalition Independent Association
Georgetown University Department of Justice Federal Trade Commission
Housing Rights Initiative terrorists


California Americas Venezuela
Iowa Michigan Asia
Pacific Arkansas North Korea
Washington U.S. North America
Myanmar Oklahoma Switzerland
Utah America Georgia
Indiana New Jersey Guam
Minnesota Alaska Germany
Washington DC Pennsylvania Florida
Illinois Africa Canada
U S Silicon Valley Cyprus
New York Washington, DC Russia
DC Texas Iran
US Christchurch France
Colorado Syria Ohio
Virginia Connecticut New Zealand
Tennessee South Dakota North Carolina
Missouri Massachusetts Turkey
United States of America China Europe
Kentucky United States Maryland
District Qatar


The President Colibra XRP
American Nazi Zuck Buck African Americans
AI Americans Indian Muslims
Stump Russian Anti
Sarbanes-Oxley American Dune
Libra Chinese Libra Project
Green New Deal Patriot Act Iranian
Libra White Paper Republicans Future
Russians Venezuelan Democrats
Democratic Hispanics