Audience

Companies and organizations searching for an AI-based natural language processing and machine learning entity extraction solution

About NetOwl Extractor

NetOwl Extractor offers highly accurate, fast, and scalable entity extraction in multiple languages using AI-based natural language processing and machine learning technologies. NetOwl's named entity recognition software can be deployed on premises or in the cloud, enabling a variety of Big Data Text Analytics applications. With over 100 types of entities, NetOwl offers a broad semantic ontology for entity extraction that goes beyond that of standard named entity extraction software. It includes people, various types of organizations (e.g., companies, governments), several types of places (e.g., countries, cities), addresses, artifacts, phone numbers, titles, etc. This expansive named entity recognition (NER) forms the foundation for more advanced relationship extraction and event extraction. Domains include Business, Finance, Politics, Homeland Security, Law Enforcement, Military, National Security, and Social Media.

Integrations

API:
Yes, NetOwl Extractor offers API access

Ratings/Reviews

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Company Information

NetOwl
Founded: 1996
United States
www.netowl.com/entity-extraction

Videos and Screen Captures

NetOwl Extractor Screenshot 1
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Product Details

Platforms Supported
Cloud
Training
Documentation
In Person
Support
Phone Support
Online

NetOwl Extractor Frequently Asked Questions

Q: What kinds of users and organization types does NetOwl Extractor work with?
Q: What languages does NetOwl Extractor support in their product?
Q: What kind of support options does NetOwl Extractor offer?
Q: What other applications or services does NetOwl Extractor integrate with?
Q: Does NetOwl Extractor have an API?
Q: What type of training does NetOwl Extractor provide?

NetOwl Extractor Product Features

Data Extraction

Pricing Extraction
Web Data Extraction
IP Address Extraction
Document Extraction
Email Address Extraction
Image Extraction
Phone Number Extraction
Disparate Data Collection