Named Entity Extractor
Extract person names, organizations, locations, and other entities from any text using a real AI model (BERT NER) running directly in your browser. Document analysis happens entirely in your browser — nothing is uploaded.
How Named Entity Extractor Works
Extract person names, organizations, locations, and dates from any text using AI. browser-only NER. Powered by BERT model locally. Use the tool above to get your results instantly — everything runs in your browser with no data sent to any server.
Named Entity Recognition Online — Free, Private, Browser-Only
Named Entity Recognition (NER) is one of the most useful natural language processing tasks. It automatically identifies and classifies key information in text: person names, company names, geographic locations, dates, monetary amounts, and more. This tool brings that capability directly to your browser with no server required.
Powered by a BERT-based NER model running via Transformers.js, this tool processes your text entirely on your device. Whether you are reviewing legal documents, analyzing news articles, or extracting contact information from emails, your data stays private.
What Gets Extracted
- Person names (PER) — People mentioned in the text
- Organizations (ORG) — Companies, institutions, agencies
- Locations (LOC) — Cities, countries, addresses, geographic places
- Miscellaneous (MISC) — Nationalities, events, products, other named entities
Use Cases for Entity Extraction
Legal document review: Quickly identify all parties, locations, and organizations mentioned in contracts, court filings, or regulatory documents. Journalism: Extract sources, organizations, and locations from press releases or interview transcripts. Research: Pull structured data from unstructured academic papers or reports. Data extraction: Convert free-text records into structured data for databases or spreadsheets.
Color-Coded Highlighting
Entities are highlighted directly in your original text with color coding: blue for people, green for organizations, amber for locations, and purple for other entities. You can export the results as a CSV file or a plain list for use in spreadsheets or other tools.
Privacy You Can Verify
Open your browser's Network tab in developer tools and watch: after the model downloads, zero network requests are made when you extract entities. Your text never leaves your machine. This makes it safe for sensitive documents like HR records, medical notes, or confidential business communications.
Frequently Asked Questions
What is Named Entity Recognition (NER)?
NER is an AI technique that identifies and classifies named entities in text into categories like person names, organizations, locations, dates, and more. It is a fundamental NLP task used in information extraction.
Is my text kept private?
Yes. Document analysis happens entirely in your browser. The BERT NER model runs locally via Transformers.js. Your text is never sent to any server.
What types of entities does it detect?
The model detects four types: Person names (PER), Organizations (ORG), Locations (LOC), and Miscellaneous entities (MISC) like nationalities, events, and products.
Does it work in all browsers?
Yes. Unlike Chrome-only AI features, this tool uses Transformers.js which works in Chrome, Firefox, Safari, Edge, and other modern browsers.
Can I export the extracted entities?
Yes. You can copy entities as a CSV file (for spreadsheets) or as a formatted list. Just click the export buttons below the results.
How accurate is the extraction?
The BERT NER model is highly accurate for standard English text. Accuracy may vary for very informal text, uncommon names, or domain-specific terminology. Each entity shows a confidence score.
Does it work offline?
Yes. After the initial model download (~170 MB), the model is cached in your browser and works without an internet connection.