Named Entity Recognition (NER)
The process of identifying real-world objects, such as persons, locations, and organizations, within unstructured text and classifying them into predefined categories.
AI Entity Extractor (NER)
Extract people, organizations, locations, and other named entities from text using an AI token classification model. 100% private and browser-based.
Last updated
100% Private Analysis
Your text is analyzed entirely in your browser using Web Workers. No data is sent to external servers. The AI model (~110MB) downloads once and is cached locally.
Preparing AI model...
PER ORG LOC MISC
Analysis results will appear here...
Entity Breakdown
0
Persons
0
Organizations
0
Locations
0
Miscellaneous
Key Concepts
Essential terms and definitions related to AI Entity Extractor (NER).
Token Classification
A machine learning task where each word (or token) in a sentence is assigned a label. NER is a prime example of token classification.
BERT Model
Bidirectional Encoder Representations from Transformers. A highly advanced language model developed by Google that understands the context of words in search queries and texts.
Frequently Asked Questions
What is Named Entity Recognition (NER)?
Named Entity Recognition is a Natural Language Processing (NLP) technique that automatically scans text to identify and categorize specific entities like names of people (PER), organizations (ORG), locations (LOC), and miscellaneous entities (MISC) like events or nationalities.
Is my data safe?
Absolutely. This tool uses Transformers.js to run the BERT model directly in your browser. Your text is analyzed locally and never sent to any external server.
What do the colored labels mean?
The tool highlights different entities in different colors to make them easy to spot: Blue for Persons (PER), Green for Locations (LOC), Purple for Organizations (ORG), and Orange for Miscellaneous (MISC) entities.
Troubleshooting & Technical Tips
Common errors developers encounter and how to resolve them.
The AI missed a clear entity name
The model relies on capitalization and context. If a name or organization is written in all lowercase (e.g., "apple inc"), the AI might struggle to recognize it. Try formatting your text with proper nouns capitalized.
Initial model download is slow
The BERT-NER model is around 40-60MB. Depending on your internet connection, the first load might take a minute. It will be cached for instant use in the future.
Related Guides
In-depth articles covering the concepts behind AI Entity Extractor (NER).