SYNTHETIC CRAFTING OF TRAINING AND TEST DATA FOR NAMED ENTITY RECOGNITION BY UTILIZING A RULE-BASED LIBRARY

A method and system for extracting and labeling Named-Entity Recognition (NER) data in a target language for use in a multi-lingual software module has been developed. First, a textual sentence is translated to the target language using a translation module. A named entity is identified and extracted within the translated sentence. The named entity is identified by either: exact mapping; a semantically similar translated named entity that meets a predetermined minimum threshold of similarity; or utilizing a rule-based library for the target language. Once identified, the named entity is labeled with a pre-determined category and stored in a retrievable electronic database.