NetOwl Extractor offers the best-of-breed named entity extraction, relationship and event extraction, geotagging, and sentiment analysis in multiple languages based on over a decade of advanced research and development. Using sophisticated computational linguistics and natural language processing technologies, NetOwl Extractor accurately finds and classifies key entities, events, and relationships in unstructured text.
NetOwl Extractor supports multiple domains and languages and also features Smart Geotagging for intelligent exploitation of geo-codable information found in text. NetOwl Extractor can be customized to perform specialized entity extraction and has broad applications in many industries, including intelligence analysis, social media analysis, competitive intelligence, enterprise information management, e-discovery, and life sciences research.
NetOwl Extractor’s state-of-the-art accuracy and high throughput, combined with the latest cloud computing architectures using frameworks such as Hadoop and HPCC (High Performance Computing Cluster), makes advanced Big Data Analysis a reality for unstructured text.
NetOwl Extractor offers a broad semantic ontology for entity extraction that goes far beyond that of standard named entity recognition. This extensive ontology has been developed in partnership with subject matter experts. The built-in ontology includes not only a variety of entities but also links and events.
NetOwl Extractor offers geotagging capabilities, in addition to entity extraction, to intelligently exploit geo-codable entities found in text.
NetOwl Extractor offers English translation of entities found by entity extraction from foreign language texts.
Entity Extraction Customization
NetOwl Extractor’s Creator Edition (CE) lets its users perform extraction customization of entities, links, and events.
NetOwl Extractor recognizes and classifies concepts using linguistic context. This sophisticated feature distinguishes semantic differences like:
- “Bush” (person) vs. “bush” (plant)
- “Jordan” place vs. person (e.g., “Michael Jordan)
- “fire” a weapon vs. “fire” a person
NetOwl Extractor resolves co-referring extracted entities, whether they are name aliases, pronouns, or definite noun phrases, identifying them as referring to the same object. For example:
- “FAA” => “Federal Aviation Administration”
- “The company’s Chairman of the Board” => “John Smith”
- Big Data Analysis for unstructured text
- Advanced entity extraction
- Unique link and event extraction capabilities
- Intelligent geotagging