Multi-Lingual Semantic Author Identification (MLSAI) Navy SBIR FY2012.1 Sol No.: Navy SBIR FY2012.1 Topic No.: N121-080 Topic Title: Multi-Lingual Semantic Author Identification (MLSAI) Proposal No.: N121-080-0829 Firm: BCL Technologies 3031 Tisch Way Suite 1000 San Jose, California 95128-2533 Contact: Hassan Alam Phone: (408) 249-4126 Web Site: www.bcltechnologies.com Abstract: n its SBIR Phase I work, BCL Technologies, using deep syntactic-semantic feature analysis of the text, will research and develop a baseline proof-of-concept system that would (1) extract syntactic, semantic and cognitive features from the text using SVM classifier techniques; and (2) identify the author of the text based on the extracted features in unstructured text on the internet (blog posts and speech transcript) in Arabic (Phase I) and Pashto (Phase I Option). Benefits: Identifying text authors will help Intelligence and Law Enforcement agencies in anti-terrorism and criminal investigations. Similarly, in the commercial world, identifying the author will allow improved search of information stream. Authoritative authors lend credence and importance to text subjects they are familiar with. Similarly, legal search can be improved by looking for authors and plagiarism can be minimized. BCL estimates the served market to be over $40 Billion annually. Return