Eliminate the costs, time and hassles of manual classification and let Semaphore Classification Server analyze and auto-classify your content. Classification Server combines precise rulebases and natural language processing to give users the best of both worlds – precise, complete and consistent classification results with the ease of automation.
Classification Server uses multiple strategies to precisely describe the information within your content to unlock its hidden value:
These sophisticated classification strategies help global organizations leverage the valuable information within their organization to make key business decisions that manage information, improve analytics and drive innovation
Semaphore Classification Server uses NLP and sophisticated semantic techniques to drive precise and consistent metadata tagging. Classification Server applies these techniques in a precise manner to ensure the highest quality results.
Semaphore’s classification process is a combination of linguistic and rule-based logic that examines information assets and uses rules and sophisticated classification strategies to apply precise and consistent metadata tags to improve search and retrieval in content management, search engine, database and workflow systems.
Semaphore’s Classification Review Tool allows you to examine classification results using sample content to identify inconsistencies, anomalies and opportunities for ontology, rulebase or classification strategy refinement. Using an agile approach changes can be made, content re-classified and results examined to ensure the highest quality metadata is returned.
Classification Analysis tool provides you with in-depth classification results for a single document. Information about how concepts, labels and relationships with a document contribute to results and how the combination of concepts and their weight result in an overall document score can be quickly identified. This process allows you to review results and make adjustments to your rules and model to increase the precision of classification output.
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