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.
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. […]
When the Rosette® Name Translator team set out to build a Hebrew-to-Latin character translator, one of the first considerations was: Which 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 Experts […]
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 […]
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 […]
We’re thrilled to announce the latest version of Rosette (1.12). This release features many exciting updates to our text analytics platform, including expanded language coverage, better accuracy, as well as new options for software delivery. Entities: Linking expanded to more languages and better Korean We’ve devoted a lot of focus to improving our support for […]
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 […]
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 […]
Rosette Entity Linking adds real-time, human-in-the-loop feature to entity linking databases While entity extraction provides the foundation of data mining and information extraction systems, extracted entities only have limited value out of context. Understanding not just what entity strings are included in your data but also the real-world entity they link back to is vital […]
Who’s in your data, and how are they connected? You may have heard about relationship extraction and wondered what this NLP innovation is. Relationship extraction is the automated detection and classification of semantic relationships between entities in text. It goes beyond automatically adding metadata to articles, to “writing” profiles and reports about a person, place, […]