Tagging Engine
Add Structure to Unstructured Datasets with Tags & Taxonomies
Tagging and extracting data on the most important keywords and concepts in your unstructured datasets gives you tremendous insight into the relevance of a content source, provides contextual navigation and helps users focus on the information they need to know now — and what they can afford to ignore.
InfoNgen’s powerful content tagging engine can automatically extract the following information from any source:
Standard Business Taxonomies
Companies, Industries, Geography, Executives, Corporate Topics
Domain-Specific Topical Taxonomies
Technology, Energy, Financial, Pharma & Life Sciences, Automotive, Agricultural, Chemical, Legal
Custom-Built Topics
Value-Based Care, Human Genetic Therapies, etc.
Powerful Features & Functionality For Content Tagging
Using natural language processing and machine learning technology, InfoNgen enriches text prior to tagging based on predefined rules for different ontologies, industries and topics with 600,000+ pre-tagged entities and topics. In order to assign relevancy and extract relationships from unstructured data, InfoNgen comes with the following features and functionality:
Entity & Topic Recognition
Document Summary Via Contextual Metadata
Search & Tagging API
- HEAR FROM THE CUSTOMER
“InfoNgen at first attracted us with its unique, custom-built taxonomy of sources, subjects, geographies, legal topics and more that assists in removing the noise from our web searches and alerts. They have retained our business through their quality customer service and ongoing, responsive product development. I appreciate that the InfoNgen team makes time for us to ensure that we get the most out of the product that they have to offer."
Julie Bozzell
Chief Research & Knowledge Services Officer, Hogan Lovells