Intelligence agencies can find people, organizations from incoming data and link to their own knowledge base to reveal insights, links between cases, and accelerate investigations.
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.
Adapt Rosette’s Entity Extraction to Your Content for Increased Accuracy
Entity extraction is becoming a mission-critical tool for finding mentions of people, places, organizations, and products in massive quantities of text. In patent searches, law enforcement, voice-of-the-customer analysis, ad targeting, content recommendation, e-discovery, and anti-fraud, entity extraction enables swift analysis of gigabytes of data. Among named entity recognition systems, those such as Rosette’s entity extraction function which […]
What is Entity Extraction?
How it’s used and how it works Entity extraction (aka, named entity recognition or NER) is a type of natural language processing technology that enables computers to analyze text as it is naturally written. Specifically, it pulls out the most important data points (entities) in unstructured text (think news, webpages, text fields). Entities include names […]
Linking In-house and Third-party Data to Streamline Success
Are you bumping into the walls of siloed data? Whether your company is grappling with legacy systems that don’t integrate or datasets from third-party sources without common keys, there are ways to create matches in the data that paint a more robust view of your customer, prospect, or person of interest. Consolidating Your In-house Data […]
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
Solid annotation guidelines are an essential requirement for producing good training data. These guidelines distinguish correct from incorrect results, define the task and ensure that the annotation process is reliable and repeatable for independent human annotators.
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 1.12: New Languages for Entity Linking, Better Korean Named Entity Extraction, and Delivery via Docker!
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 […]
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 […]
Provide Live Feedback to Your Entity Linking Knowledge Bases
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 […]
Understanding the Difference Between Open and Targeted Relationship Extraction
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, […]
Rosette Cloud 1.9: More Languages, Higher Accuracy, and Deep Neural Nets
Rosette Cloud 1.9 is out, delivering a new language for name matching, translation, and deduplication: Thai. We’ve also added a new deep neural network model for sentiment analysis, entity extraction offsets, salience scores for topic extraction, and more. Learn more below, or jump to the release notes. Name Matching The /name-similarity, /name-translation, and /name-deduplication endpoints […]
Just the Important Entities, Please
Salience scores and linking confidence scores for extracted entities come to Rosette Cloud Data scraped from the web is often very noisy and cumbersome to work with. Sorting through it to find the most valuable information is a vital step in converting raw data into actionable insights. The release of Rosette Cloud 1.8 aims to […]
Relax, Your Sensitive Data Is Secure
A new Rosette Cloud script enables you to hide personally identifying information (PII) in your documents and data Often organizations need to share documents and information that may include personally identifiable information, whether out of good conscience or by legal mandate. Going through documents manually to identify and remove all potentially compromisable data is time […]