Text Analytics
K-Extractor™: Convert text into actionable knowledge
Customer Scenario
Need
Gain specific knowledge from a collection of documents. Identify concepts of interest and their connections, or look up specific events. Enable federated search for both structured and unstructured data resources.
Relying on analysts reading through the collection of documents is not feasible anymore. Big Data volumes of unstructured text are impossible to convert to the valuable asset without automation. The automated tools based on shallow concept recognition are throwing away valuable knowledge.
Challenge
Lymba's Solution
Solution
K-Extractor™ identifies concepts and events of interest as well as a variety of semantic relations between them. Originally developed to meet the needs of the US Government, it provides event-centric knowledge out of the box. Its powerful inference capabilities allow customizations that satisfy various domain-specific knowledge needs. K-Extractor organizes any unstructured content into a deep semantic structure.
Input
Text in virtually any format: from scanned documents in PDF format to plain text files, from popular Office formats to Web pages and social media streams.
Deep semantic representation: a wide variety of concepts and the semantic relations that link them - in a customizable RDF format.
Output
Key Features
Out-of-the-Box Capability:
Powerful out-of-the-box extraction for a variety of concept types, including people, organizations, events, locations, times, quantities, and many more.
Deep Natural Language Understanding:
Accurate identification of more than 26 semantic relation types to precisely pinpoint the meaning of text, including co-referring links for expressions that refer to the same person or thing.
Configurable & Expandable:
Rapid tailoring for new domains or different knowledge needs by easily adding new concept and relation types.
Check out K-Extractor™ for more details on the product and its features.
Or request a demo with us: