Tag: Entity Extraction

Entity extraction  is the process of identifying words in a given text that refer to people, places, products, organizations, etc. by using different extraction methods such as statistical or deep neural network processors, exact match processors, and pattern matching processors. When used together with entity resolution, the extracted words can be mapped to real life entities.

You can find our recent articles about entity extraction on this page.

NLP Boosts Geopolitical Forecasting

How the KaDSci Team leveraged NLP to jump into second place in IARPA competition Basis Technology congratulates the authors of the peer-reviewed article “What do forecasting rationales reveal about thinking patterns of top geopolitical forecasters?”, which delves into the technical details of how the KaDSci team leapt from 27th place to 2nd place in three […]

Top 5 Takeaways from AI for Human Language

AI for Human Language 2021 brought together hundreds of professionals in cybersecurity, financial security, and compliance to explore technology available today that is enabling us to verify identities and anticipate world events. In this (almost in-person) virtual experience, speakers from IDF Unit 8200, Cybersixgill, Recorded Future, Metis Augmented Intelligence, and more came together to demonstrate […]

How to Write Annotation Guidelines for Entity Extraction

Writing annotation guidelines for entity extraction for adverse media screening in financial compliance As much as we love to think our machine learning algorithms are the most important part of training up a model, they need human-annotated data to get started, which also — very importantly — are the benchmark for evaluating a model’s accuracy. […]

Building a More Useful Hebrew Transliteration Scheme

When the Rosette® Name Translator team set out to build a Hebrew-to-Latin character translator, one of the first considerations was: Which Hebrew transliteration standard should we use? As the joke goes, “Standards are great because there are so many to choose from.” The existing Hebrew transliteration standards, ISO 259-2:1994 and UNGEGN (United Nations Group of […]

Faster Annotation with Rosette Adaptation Studio

What are the top three barriers to better machine learning models? Annotating data, annotating data, and annotating data. Okay, so it’s not that simple, but producing quality training data to produce accurate models takes up the lion’s share of human labor and time in the entire process. This includes collecting and cleaning data, making sure […]

What’s the Difference Between Entity Extraction (NER) and Entity Resolution?

Entity extraction, or named entity recognition (NER), is finding mentions of key “things” (aka “entities”) such as people, places, organizations, dates, and time within text. Entity mentions are the words in text that refer to entities, such as “Bill Clinton,” “White House,” and “U.S.” Entity resolution (aka, entity linking) takes it one step further and […]

Rosette Cloud 1.11: New Entity Types, Hungarian Names, and Cross Language Semantics

We’re thrilled to announce the latest version of Rosette (1.11). It’s a big one — lots of exciting new features, enhancements, and improvements. We hope you’ll check it out! TL; DR check the release notes. Entities: Enhanced Extraction and Linking with New Types Rosette Entity Extraction & Linking now recognizes 700 new classes of entities […]

The Most Effective Entity Extraction Techniques

A hybrid of entity extraction methods to compensate for various strengths and weaknesses Just as you would never use a screwdriver to insert a nail, each type of entity is most accurately extracted by a different approach. There are many ways to extract entities, but no one universal solution for all entities. Different extraction methods […]